Collate CPUE data
Max Lindmark
24 November, 2022
Intro
In this script, I load exchange data from datras and calculate catch of cod and flounder in unit kg/km^2 (with TVL gear) by size group, by correcting for gear dimensions, sweeplength and trawl speed, following Orio et al 2017.
Load libraries
library(tidyverse)
#> Warning: package 'tidyr' was built under R version 4.0.5
library(readxl)
library(tidylog)
library(RCurl)
library(viridis)
library(RColorBrewer)
#> Warning: package 'RColorBrewer' was built under R version 4.0.5
library(patchwork)
library(janitor)
library(icesDatras)
library(mapdata)
library(patchwork)
library(rgdal)
library(raster)
library(sf)
library(rgeos)
library(chron)
library(lattice)
library(ncdf4)
library(marmap)
library(rnaturalearth)
library(rnaturalearthdata)
library(mapplots)
library(geosphere)
library(modelr)
world <- ne_countries(scale = "medium", returnclass = "sf")
# function to plot proportion zero catches
prop_zero <- function(.data){
.data %>%
mutate(zero_catch = ifelse(haul_cpue_kg == 0, "Y", "N")) %>%
group_by(zero_catch) %>%
summarise(n = n()) %>%
mutate(per = prop.table(n) * 100)
}
# Source code for map plots
source("/Users/maxlindmark/Dropbox/Max work/R/cod_interactions/R/functions/map_plot.R")
# Source code for lon lat to utm
source("/Users/maxlindmark/Dropbox/Max work/R/cod_interactions/R/functions/lon_lat_utm.R")
theme_set(theme_plot())
# Continuous colors
options(ggplot2.continuous.colour = "viridis")
# Discrete colors
scale_colour_discrete <- function(...) {
scale_colour_brewer(palette = "Set1")
}
scale_fill_discrete <- function(...) {
scale_fill_brewer(palette = "Set1")
}Read data
# Data were read in from getDATRAS on 2022.09.06
# Read HH data
# bits_hh <- getDATRAS(record = "HH", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_hh, "data/DATRAS_exchange/bits_hh.csv")
bits_hh <- read.csv("data/DATRAS_exchange/bits_hh.csv") %>% filter(Year > 1992) # To match covariates
# Read HL data
# bits_hl <- getDATRAS(record = "HL", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_hl, "data/DATRAS_exchange/bits_hl.csv")
bits_hl <- read.csv("data/DATRAS_exchange/bits_hl.csv") %>% filter(Year > 1992) # To match covariates
# Read CA data
# bits_ca <- getDATRAS(record = "CA", survey = "BITS", years = 1991:2020, quarters = c(1, 4))
# write.csv(bits_ca, "data/DATRAS_exchange/bits_ca.csv")
bits_ca <- read.csv("data/DATRAS_exchange/bits_ca.csv") %>% filter(Year > 1992) # To match covariates
# Read gear standardization data
sweep <- read.csv("data/from_ale/sweep_9116.csv", sep = ";", dec = ",", fileEncoding = "latin1")
sweep <- read.csv("data/from_ale/sweep_9118_ml.csv", sep = ";", fileEncoding = "latin1")Standardize catch data
Standardize ships
# Before creating a a new ID, make sure that countries and ships names use the same format
sort(unique(sweep$Ship))
#> [1] "26HF" "ATL" "ATLD" "BAL" "BALL" "BPE" "CEV" "CLP" "CLV" "COML"
#> [11] "DAN2" "DANS" "DAR" "GDY" "HAF" "KOH" "KOOT" "MON" "MONL" "SOL"
#> [21] "SOL2" "VSH" "ZBA"
sort(unique(bits_hh$Ship))
#> [1] "06JR" "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77MA" "77SE"
#> [11] "AA36" "ESLF" "ESOR" "ESTM" "LAIZ" "LTDA" "RUEK" "RUJB" "RUNT" "RUS6"
sort(unique(bits_hl$Ship))
#> [1] "06JR" "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77MA" "77SE"
#> [11] "AA36" "ESLF" "ESOR" "ESTM" "LAIZ" "LTDA" "RUEK" "RUJB" "RUNT" "RUS6"
# Change back to the old Ship name standard...
# https://vocab.ices.dk/?ref=315
# https://vocab.ices.dk/?ref=315
# Assumptions:
# SOL is Solea on ICES links above, and SOL1 is the older one of the two SOLs (1 and 2)
# DAN is Dana
# sweep %>% filter(Ship == "DANS") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "67BC") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "26D4") %>% distinct(Year) # Strange that 26DF doesn't extend far back. Which ship did the Danes use? Ok, I have no Danish data that old.
# bits_hh %>% filter(Country == "DK") %>% distinct(Year)
bits_hh <- bits_hh %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
bits_hl <- bits_hl %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
bits_ca <- bits_ca %>%
mutate(Ship2 = fct_recode(Ship,
"SOL" = "06S1",
"SOL2" = "06SL",
"DAN2" = "26D4",
"HAF" = "26HF",
"HAF" = "26HI",
"HAF" = "67BC",
"BAL" = "67BC",
"ARG" = "77AR",
"77SE" = "77SE",
"AA36" = "AA36",
"KOOT" = "ESLF",
"KOH" = "ESTM",
"DAR" = "LTDA",
"ATLD" = "RUJB",
"ATL" = "RUNT"),
Ship2 = as.character(Ship2)) %>%
mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))
# Ok, which ships are missing in the exchange data?
unique(bits_hh$Ship3)[!unique(bits_hh$Ship3) %in% unique(sweep$Ship)]
#> [1] "LAIZ" "AA36" "06JR" "ARG" "RUEK" "RUS6" "77MA" "77SE" "ESOR"
# Swedish Ships and unidentified ships are NOT in the Sweep data
unique(sweep$Ship3)[!unique(sweep$Ship3) %in% unique(bits_hh$Ship3)]
#> NULL
# But all Sweep Ships are in the exchange dataStandardize countries
# Now check which country codes are used
sort(unique(sweep$Country))
#> [1] "DEN" "EST" "GFR" "LAT" "LTU" "POL" "RUS" "SWE"
sort(unique(bits_hh$Country))
#> [1] "DE" "DK" "EE" "LT" "LV" "PL" "RU" "SE"
# https://www.nationsonline.org/oneworld/country_code_list.htm#E
bits_hh <- bits_hh %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
bits_hl <- bits_hl %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
bits_ca <- bits_ca %>%
mutate(Country = fct_recode(Country,
"DEN" = "DK",
"EST" = "EE",
"GFR" = "DE",
"LAT" = "LV",
"LTU" = "LT",
"POL" = "PL",
"RUS" = "RU",
"SWE" = "SE"),
Country = as.character(Country))
# Gear? Are they the same?
sort(unique(bits_hh$Gear))
#> [1] "DT" "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "HAK" "LBT" "LPT" "P20" "PEL"
#> [13] "SON" "TVL" "TVS"
sort(unique(bits_hl$Gear))
#> [1] "DT" "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "HAK" "LBT" "P20" "PEL" "SON"
#> [13] "TVL" "TVS"
sort(unique(sweep$Gear))
#> [1] "CAM" "CHP" "DT" "EGY" "ESB" "EXP" "GRT" "H20" "HAK" "LBT" "LPT" "P20"
#> [13] "PEL" "SON" "TVL" "TVS"
# Which gears are NOT in the sweep data?
unique(bits_hl$Gear)[!unique(bits_hl$Gear) %in% unique(sweep$Gear)]
#> [1] "GOV" "FOT"Create a simple haul ID that works across all exchange data
# Create ID column
bits_ca <- bits_ca %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
bits_hl <- bits_hl %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
bits_hh <- bits_hh %>%
mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
# Works like a haul-id
bits_hh %>% group_by(IDx) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 1Create the same unique haul-ID in the cpue data that I have in the sweep-file
bits_hl <- bits_hl %>%
mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":"))
bits_hh <- bits_hh %>%
mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":"))
bits_hh %>% group_by(haul.id) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 1Clean DATRAS EXCHANGE data
# Select just valid, additional and no oxygen hauls
bits_hh <- bits_hh %>%
#filter(!Country == "SWE") %>% # I'll deal with Sweden later...
filter(HaulVal %in% c("A","N","V"))
# Add ICES rectangle
bits_hh$Rect <- mapplots::ices.rect2(lon = bits_hh$ShootLong, lat = bits_hh$ShootLat)
# Add ICES subdivisions
shape <- shapefile("data/ICES_StatRec_mapto_ICES_Areas/StatRec_map_Areas_Full_20170124.shp")
pts <- SpatialPoints(cbind(bits_hh$ShootLong, bits_hh$ShootLat),
proj4string = CRS(proj4string(shape)))
#> Warning in proj4string(shape): CRS object has comment, which is lost in output
bits_hh$sub_div <- over(pts, shape)$Area_27
# Rename subdivisions to the more common names and do some more filtering (by sub div and area)
sort(unique(bits_hh$sub_div))
#> [1] "3.a.20" "3.a.21" "3.b.23" "3.c.22" "3.d.24" "3.d.25"
#> [7] "3.d.26" "3.d.27" "3.d.28.1" "3.d.28.2" "3.d.29"
bits_hh <- bits_hh %>%
mutate(sub_div = factor(sub_div),
sub_div = fct_recode(sub_div,
"20" = "3.a.20",
"21" = "3.a.21",
"22" = "3.c.22",
"23" = "3.b.23",
"24" = "3.d.24",
"25" = "3.d.25",
"26" = "3.d.26",
"27" = "3.d.27",
"28" = "3.d.28.1",
"28" = "3.d.28.2",
"29" = "3.d.29"),
sub_div = as.character(sub_div))
# Now add the fishing line information from the sweep file (we need that later
# to standardize based on gear geometry). We add in the the HH data and then
# transfer it to the other exchange data files when left_joining.
# Check which Fishing lines I have in the sweep data:
fishing_line <- sweep %>% group_by(Gear) %>% distinct(Fishing.line)
bits_hh <- left_join(bits_hh, fishing_line)
# sweep %>% group_by(Gear) %>% distinct(Fishing.line)
# bits_hh %>% group_by(Gear) %>% distinct(Fishing.line)
bits_hh$Fishing.line <- as.numeric(bits_hh$Fishing.line)
# Which gears do now have fishing line?
bits_hh$Fishing.line[is.na(bits_hh$Fishing.line)] <- -9
bits_hh %>% filter(Fishing.line == -9) %>% distinct(Gear)
#> Gear
#> 1 GRT
#> 2 FOT
#> 3 GOV
#> 4 DT
#> 5 LPT
#> 6 ESB
#> 7 EXP
#> 8 HAK
# 1 GRT
# 2 CAM
# 3 EXP
# 4 FOT
# 5 GOV
# 6 EGY
# 7 DT
# 8 ESB
# 9 HAK
# FROM the index files (Orio, "Research Östersjön 2")
# FOT has 83
# GOV has 160
# ESB ??
# GRT ??
# Rest are unknown and likely not used by Swedish data (therefore their correction
# factors my be in the sweep file)
# Add these values:
bits_hh <- bits_hh %>% mutate(Fishing.line = ifelse(Gear == "FOT", 83, Fishing.line))
bits_hh <- bits_hh %>% mutate(Fishing.line = ifelse(Gear == "GOV", 160, Fishing.line))
# Now select the hauls in the HH data when subsetting the HL data
bits_hl <- bits_hl %>%
filter(haul.id %in% bits_hh$haul.id)
# Match columns from the HH data to the HL and CA data
sort(unique(bits_hh$sub_div))
#> [1] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
sort(colnames(bits_hh))
#> [1] "BotCurDir" "BotCurSpeed" "BotSal"
#> [4] "BotTemp" "Buoyancy" "BySpecRecCode"
#> [7] "CodendMesh" "Country" "DataType"
#> [10] "DateofCalculation" "Day" "DayNight"
#> [13] "Depth" "DepthStratum" "Distance"
#> [16] "DoorSpread" "DoorSurface" "DoorType"
#> [19] "DoorWgt" "Fishing.line" "Gear"
#> [22] "GearEx" "GroundSpeed" "haul.id"
#> [25] "HaulDur" "HaulLat" "HaulLong"
#> [28] "HaulNo" "HaulVal" "HydroStNo"
#> [31] "IDx" "KiteDim" "MaxTrawlDepth"
#> [34] "MinTrawlDepth" "Month" "Netopening"
#> [37] "PelSampType" "Quarter" "RecordType"
#> [40] "Rect" "Rigging" "SecchiDepth"
#> [43] "Ship" "Ship2" "Ship3"
#> [46] "ShootLat" "ShootLong" "SpeedWater"
#> [49] "StatRec" "StdSpecRecCode" "StNo"
#> [52] "sub_div" "SurCurDir" "SurCurSpeed"
#> [55] "SurSal" "SurTemp" "Survey"
#> [58] "SweepLngt" "SwellDir" "SwellHeight"
#> [61] "ThClineDepth" "ThermoCline" "Tickler"
#> [64] "TidePhase" "TideSpeed" "TimeShot"
#> [67] "TowDir" "Turbidity" "WarpDen"
#> [70] "Warpdia" "Warplngt" "WgtGroundRope"
#> [73] "WindDir" "WindSpeed" "WingSpread"
#> [76] "X" "Year"
# No NAs for the variables going in to the stomach haul ID
unique(is.na(bits_hh %>% dplyr::select(Year, Quarter, Month, Country, Rect, HaulNo)))
#> Year Quarter Month Country Rect HaulNo
#> [1,] FALSE FALSE FALSE FALSE FALSE FALSE
# Before making the id_haul_stomach variable we need to change the country column so that it actually matches the stomach data
# This is the stomach version:
#[1] "LV" "PL" "SE" "DK"
unique(bits_hh$Country)
#> [1] "LAT" "POL" "DEN" "GFR" "SWE" "RUS" "EST" "LTU"
# MAKE SURE THE COUNTRY CODE IS THE SAME! FOR NOW I DON*T USE COUNTRY 2
bits_hh <- bits_hh %>% mutate(Country2 = NA,
Country2 = ifelse(Country == "LAT", "LV", Country2),
Country2 = ifelse(Country == "POL", "PL", Country2),
Country2 = ifelse(Country == "SWE", "SE", Country2),
Country2 = ifelse(Country == "DEN", "DK", Country2))
bits_hh_merge <- bits_hh %>%
mutate(id_haul_stomach = paste(Year, Quarter, Month, Country, Rect, HaulNo, sep = ".")) %>%
dplyr::select(sub_div, Rect, HaulVal, StdSpecRecCode, BySpecRecCode, Fishing.line, Month,
DataType, HaulDur, GroundSpeed, haul.id, IDx, ShootLat, ShootLong, id_haul_stomach)
bits_hl <- left_join(dplyr::select(bits_hl, -haul.id), bits_hh_merge, by = "IDx")
bits_ca <- left_join(bits_ca, bits_hh_merge, by = "IDx")
# Now filter the subdivisions I want from all data sets
bits_hh <- bits_hh %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_hl <- bits_hl %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_ca <- bits_ca %>% filter(sub_div %in% c(24, 25, 26, 27, 28))bits_hl %>% filter(Year == 2016 & Quarter == 1 & Month == 2 & Country == "SWE" & Rect == "39G4") %>% distinct(HaulNo)
#> filter: removed 820,236 rows (>99%), 144 rows remaining
#> distinct: removed 143 rows (99%), one row remaining
#> HaulNo
#> 1 1Filter species
hlcod <- bits_hl %>%
filter(SpecCode %in% c("126436", "164712")) %>%
mutate(Species = "Gadus morhua")
hlfle <- bits_hl %>%
filter(SpecCode %in% c("127141", "172894")) %>%
mutate(Species = "Platichthys flesus")
# Plaice is not included in the stomach study though
# hlpla <- bits_hl %>%
# filter(SpecCode %in% c("127143", "172902")) %>%
# mutate(Species = "Pleuronectes platessa")Prepare to add 0 catches
# Find common columns in the HH and HL data (here already subset by species)
comcol <- intersect(names(hlcod), names(bits_hh))
# What is the proportion of zero-catch hauls?
# Here we don't have zero catches
hlcod %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(HLNoAtLngt)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
distinct(zero_catch)
#> # A tibble: 1 × 1
#> zero_catch
#> <chr>
#> 1 N
# Cod: Add 0s and then remove lines with SpecVal = 0 (first NA because we don't have a match in the HH, then make them 0 later)
hlcod0 <- full_join(hlcod, bits_hh[, comcol], by = comcol)
# No zeroes yet
hlcod0 %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(HLNoAtLngt)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
distinct(zero_catch)
#> # A tibble: 1 × 1
#> zero_catch
#> <lgl>
#> 1 NA
hlcod0$SpecVal[is.na(hlcod0$SpecVal)] <- "zeroCatch"
hlcod0$SpecVal <- factor(hlcod0$SpecVal)
hlcod0 <- hlcod0 %>% filter(!SpecVal == "0")
# Add species again after merge
hlcod0$Species <- "Gadus morhua"
# Flounder: Add 0s, remove them if StdSpecRecCode !=1 and then remove lines with SpecVal = 0
hlfle0 <- full_join(hlfle, bits_hh[, comcol], by = comcol)
hlfle0 <- hlfle0[!(is.na(hlfle0$Species) & hlfle0$StdSpecRecCode != 1), ]
hlfle0$SpecVal[is.na(hlfle0$SpecVal)] <- "zeroCatch"
hlfle0$SpecVal <- factor(hlfle0$SpecVal)
hlfle0 <- hlfle0 %>% filter(!SpecVal == "0")
hlfle0$Species<-"Platichthys flesus"
# Check number of hauls per species
hlcod0 %>% distinct(haul.id) %>% nrow()
#> [1] 12254
hlfle0 %>% distinct(haul.id) %>% nrow()
#> [1] 12016Create (unstandardized) CPUE for SpecVal=1. If
DataType=C then CPUEun=HLNoAtLngt, if
DataType=R then
CPUEun=HLNoAtLngt/(HaulDur/60), if DataType=S
then CPUEun=(HLNoAtLngt*SubFactor)/(HaulDur/60). If
SpecVal="zeroCatch" then CPUEun=0, if
SpecVal=4 we need to decide (no length measurements, only
total catch). Note that here we also add zero CPUE if
SpecVal=="zeroCatch".
Then I will sum for the same haul the CPUE of the same length classes if they were sampled with different subfactors or with different sexes.
# Cod
hlcod0 <- hlcod0 %>%
mutate(CPUEun = ifelse(SpecVal == "1" & DataType == "C",
HLNoAtLngt,
ifelse(SpecVal == "1" & DataType == "R",
HLNoAtLngt/(HaulDur/60),
ifelse(SpecVal == "1" & DataType == "S",
(HLNoAtLngt*SubFactor)/(HaulDur/60),
ifelse(SpecVal == "zeroCatch", 0, NA)))))
# Plot and fill by zero catch
hlcod0 %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
filter(!CPUEun_haul == 0)
#> # A tibble: 10,268 × 3
#> haul.id Year CPUEun_haul
#> <chr> <int> <dbl>
#> 1 1993:1:DEN:DAN2:GRT:105:45 1993 37
#> 2 1993:1:DEN:DAN2:GRT:106:46 1993 2
#> 3 1993:1:DEN:DAN2:GRT:107:47 1993 34
#> 4 1993:1:DEN:DAN2:GRT:108:48 1993 5
#> 5 1993:1:DEN:DAN2:GRT:109:49 1993 24
#> 6 1993:1:DEN:DAN2:GRT:11:6 1993 135
#> 7 1993:1:DEN:DAN2:GRT:110:50 1993 40
#> 8 1993:1:DEN:DAN2:GRT:112:51 1993 32
#> 9 1993:1:DEN:DAN2:GRT:113:52 1993 12
#> 10 1993:1:DEN:DAN2:GRT:114:53 1993 16
#> # … with 10,258 more rows
hlcod0 %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, zero_catch) %>%
summarise(n = n()) %>%
ggplot(., aes(x = Year, y = n, fill = zero_catch)) +
geom_bar(stat = "identity")
# Some rows have multiple rows per combination of length class and haul id, so we need to sum it up
hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)
#> # A tibble: 2 × 1
#> n
#> <int>
#> 1 1
#> 2 2
hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% filter(n == 2) %>% as.data.frame() %>% head(20)
#> X RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType
#> 1 205495 HL BITS 1 RUS RUJB HAK NA <NA> NA
#> 2 300422 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 3 300423 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 4 325369 HL BITS 4 DEN 26D4 TVL NA R NA
#> 5 325375 HL BITS 4 DEN 26D4 TVL NA R NA
#> 6 326062 HL BITS 4 DEN 26D4 TVL NA R NA
#> 7 326064 HL BITS 4 DEN 26D4 TVL NA R NA
#> 8 326065 HL BITS 4 DEN 26D4 TVL NA R NA
#> 9 326066 HL BITS 4 DEN 26D4 TVL NA R NA
#> 10 326067 HL BITS 4 DEN 26D4 TVL NA R NA
#> 11 326068 HL BITS 4 DEN 26D4 TVL NA R NA
#> 12 326069 HL BITS 4 DEN 26D4 TVL NA R NA
#> 13 326071 HL BITS 4 DEN 26D4 TVL NA R NA
#> 14 326073 HL BITS 4 DEN 26D4 TVL NA R NA
#> 15 326077 HL BITS 4 DEN 26D4 TVL NA R NA
#> 16 326079 HL BITS 4 DEN 26D4 TVL NA R NA
#> 17 326080 HL BITS 4 DEN 26D4 TVL NA R NA
#> 18 326082 HL BITS 4 DEN 26D4 TVL NA R NA
#> 19 326083 HL BITS 4 DEN 26D4 TVL NA R NA
#> 20 326085 HL BITS 4 DEN 26D4 TVL NA R NA
#> StNo HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier
#> 1 <NA> 10 1998 T 164712 4 <NA> -9 1
#> 2 14 14 2000 T 164712 1 M 2 1
#> 3 14 14 2000 T 164712 1 F 4 1
#> 4 63 31 2001 T 164712 1 M 5 1
#> 5 63 31 2001 T 164712 1 F 7 1
#> 6 83 37 2001 T 164712 1 M 21 1
#> 7 83 37 2001 T 164712 1 M 21 1
#> 8 83 37 2001 T 164712 1 M 21 1
#> 9 83 37 2001 T 164712 1 M 21 1
#> 10 83 37 2001 T 164712 1 M 21 1
#> 11 83 37 2001 T 164712 1 M 21 1
#> 12 83 37 2001 T 164712 1 M 21 1
#> 13 83 37 2001 T 164712 1 M 21 1
#> 14 83 37 2001 T 164712 1 M 21 1
#> 15 83 37 2001 T 164712 1 F 42 1
#> 16 83 37 2001 T 164712 1 F 42 1
#> 17 83 37 2001 T 164712 1 F 42 1
#> 18 83 37 2001 T 164712 1 F 42 1
#> 19 83 37 2001 T 164712 1 F 42 1
#> 20 83 37 2001 T 164712 1 F 42 1
#> NoMeas SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 NA 1 NA NA <NA> NA -9 <NA>
#> 2 3 1 NA 32 1 35 2 <NA>
#> 3 3 1 NA 32 1 35 2 <NA>
#> 4 5 1 NA 3142 1 22 2 <NA>
#> 5 7 1 NA 3142 1 22 1 <NA>
#> 6 21 1 NA 64261 1 22 1 <NA>
#> 7 21 1 NA 64261 1 38 2 <NA>
#> 8 21 1 NA 64261 1 39 2 <NA>
#> 9 21 1 NA 64261 1 41 1 <NA>
#> 10 21 1 NA 64261 1 42 1 <NA>
#> 11 21 1 NA 64261 1 44 2 <NA>
#> 12 21 1 NA 64261 1 45 4 <NA>
#> 13 21 1 NA 64261 1 48 2 <NA>
#> 14 21 1 NA 64261 1 52 1 <NA>
#> 15 42 1 NA 64261 1 22 1 <NA>
#> 16 42 1 NA 64261 1 38 2 <NA>
#> 17 42 1 NA 64261 1 39 2 <NA>
#> 18 42 1 NA 64261 1 41 1 <NA>
#> 19 42 1 NA 64261 1 42 4 <NA>
#> 20 42 1 NA 64261 1 44 3 <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20140617 126436 ATLD ATLD
#> 2 NA 20131112 126436 KOOT KOOT
#> 3 NA 20131112 126436 KOOT KOOT
#> 4 NA 20131113 126436 DAN2 DAN2
#> 5 NA 20131113 126436 DAN2 DAN2
#> 6 NA 20131113 126436 DAN2 DAN2
#> 7 NA 20131113 126436 DAN2 DAN2
#> 8 NA 20131113 126436 DAN2 DAN2
#> 9 NA 20131113 126436 DAN2 DAN2
#> 10 NA 20131113 126436 DAN2 DAN2
#> 11 NA 20131113 126436 DAN2 DAN2
#> 12 NA 20131113 126436 DAN2 DAN2
#> 13 NA 20131113 126436 DAN2 DAN2
#> 14 NA 20131113 126436 DAN2 DAN2
#> 15 NA 20131113 126436 DAN2 DAN2
#> 16 NA 20131113 126436 DAN2 DAN2
#> 17 NA 20131113 126436 DAN2 DAN2
#> 18 NA 20131113 126436 DAN2 DAN2
#> 19 NA 20131113 126436 DAN2 DAN2
#> 20 NA 20131113 126436 DAN2 DAN2
#> IDx sub_div Rect HaulVal StdSpecRecCode BySpecRecCode
#> 1 1998.1.RUS.RUJB.HAK.NA.10 26 38G9 V 1 1
#> 2 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 3 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 4 2001.4.DEN.26D4.TVL.63.31 26 39G8 V 1 1
#> 5 2001.4.DEN.26D4.TVL.63.31 26 39G8 V 1 1
#> 6 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 7 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 8 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 9 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 10 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 11 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 12 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 13 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 14 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 15 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 16 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 17 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 18 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 19 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> 20 2001.4.DEN.26D4.TVL.83.37 26 40G8 V 1 1
#> Fishing.line Month DataType HaulDur GroundSpeed haul.id
#> 1 -9.00 3 C 30 3.8 1998:1:RUS:ATLD:HAK:NA:10
#> 2 33.22 11 C 30 3.0 2000:4:EST:KOOT:TVS:14:14
#> 3 33.22 11 C 30 3.0 2000:4:EST:KOOT:TVS:14:14
#> 4 63.46 11 R 30 3.0 2001:4:DEN:DAN2:TVL:63:31
#> 5 63.46 11 R 30 3.0 2001:4:DEN:DAN2:TVL:63:31
#> 6 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 7 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 8 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 9 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 10 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 11 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 12 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 13 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 14 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 15 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 16 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 17 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 18 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 19 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> 20 63.46 11 R 31 3.1 2001:4:DEN:DAN2:TVL:83:37
#> ShootLat ShootLong id_haul_stomach Species CPUEun n
#> 1 54.6333 19.6500 1998.1.3.RUS.38G9.10 Gadus morhua NA 2
#> 2 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2.000000 2
#> 3 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2.000000 2
#> 4 55.4699 18.3116 2001.4.11.DEN.39G8.31 Gadus morhua 4.000000 2
#> 5 55.4699 18.3116 2001.4.11.DEN.39G8.31 Gadus morhua 2.000000 2
#> 6 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 7 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 8 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 9 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 10 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 11 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 12 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 7.741935 2
#> 13 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 14 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 15 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 16 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 17 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 3.870968 2
#> 18 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 1.935484 2
#> 19 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 7.741935 2
#> 20 55.6627 18.0414 2001.4.11.DEN.40G8.37 Gadus morhua 5.806452 2
test <- hlcod0 %>% group_by(LngtClass, haul.id) %>% mutate(n = n()) %>% ungroup() %>% filter(n == 2)
test_id <- test$haul.id[2]
hlcodL <- hlcod0 %>%
group_by(LngtClass, haul.id) %>%
mutate(CPUEun = sum(CPUEun)) %>%
ungroup() %>%
mutate(id3 = paste(haul.id, LngtClass)) %>%
distinct(id3, .keep_all = TRUE) %>%
dplyr::select(-X, -id3) # Clean up a bit
# Check with an ID
filter(hlcod0, haul.id == test_id)
#> X RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType
#> 1 300422 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 2 300423 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 3 300424 HL BITS 4 EST ESLF TVS NA <NA> NA
#> 4 4998 HH BITS 4 EST ESLF TVS NA <NA> NA
#> StNo HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier
#> 1 14 14 2000 T 164712 1 M 2 1
#> 2 14 14 2000 T 164712 1 F 4 1
#> 3 14 14 2000 T 164712 1 F 4 1
#> 4 14 14 2000 <NA> NA zeroCatch <NA> NA NA
#> NoMeas SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 3 1 NA 32 1 35 2 <NA>
#> 2 3 1 NA 32 1 35 2 <NA>
#> 3 3 1 NA 32 1 39 2 <NA>
#> 4 NA NA NA NA <NA> NA NA <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20131112 126436 KOOT KOOT
#> 2 NA 20131112 126436 KOOT KOOT
#> 3 NA 20131112 126436 KOOT KOOT
#> 4 NA 20220301 NA KOOT KOOT
#> IDx sub_div Rect HaulVal StdSpecRecCode BySpecRecCode
#> 1 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 2 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 3 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 4 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> Fishing.line Month DataType HaulDur GroundSpeed haul.id
#> 1 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> 2 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> 3 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> 4 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> ShootLat ShootLong id_haul_stomach Species CPUEun
#> 1 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2
#> 2 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2
#> 3 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2
#> 4 58.0167 21.0833 <NA> Gadus morhua 0
filter(hlcodL, haul.id == test_id) %>% as.data.frame()
#> RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType StNo
#> 1 HL BITS 4 EST ESLF TVS NA <NA> NA 14
#> 2 HL BITS 4 EST ESLF TVS NA <NA> NA 14
#> 3 HH BITS 4 EST ESLF TVS NA <NA> NA 14
#> HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier NoMeas
#> 1 14 2000 T 164712 1 M 2 1 3
#> 2 14 2000 T 164712 1 F 4 1 3
#> 3 14 2000 <NA> NA zeroCatch <NA> NA NA NA
#> SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 1 NA 32 1 35 2 <NA>
#> 2 1 NA 32 1 39 2 <NA>
#> 3 NA NA NA <NA> NA NA <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20131112 126436 KOOT KOOT
#> 2 NA 20131112 126436 KOOT KOOT
#> 3 NA 20220301 NA KOOT KOOT
#> IDx sub_div Rect HaulVal StdSpecRecCode BySpecRecCode
#> 1 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 2 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> 3 2000.4.EST.ESLF.TVS.14.14 28 45H1 V 1 1
#> Fishing.line Month DataType HaulDur GroundSpeed haul.id
#> 1 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> 2 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> 3 33.22 11 C 30 3 2000:4:EST:KOOT:TVS:14:14
#> ShootLat ShootLong id_haul_stomach Species CPUEun
#> 1 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 4
#> 2 58.0167 21.0833 2000.4.11.EST.45H1.14 Gadus morhua 2
#> 3 58.0167 21.0833 <NA> Gadus morhua 0
# Do we still have 0 catches?
hlcodL %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
filter(!CPUEun_haul == 0)
#> # A tibble: 10,268 × 3
#> haul.id Year CPUEun_haul
#> <chr> <int> <dbl>
#> 1 1993:1:DEN:DAN2:GRT:105:45 1993 37
#> 2 1993:1:DEN:DAN2:GRT:106:46 1993 2
#> 3 1993:1:DEN:DAN2:GRT:107:47 1993 34
#> 4 1993:1:DEN:DAN2:GRT:108:48 1993 5
#> 5 1993:1:DEN:DAN2:GRT:109:49 1993 24
#> 6 1993:1:DEN:DAN2:GRT:11:6 1993 135
#> 7 1993:1:DEN:DAN2:GRT:110:50 1993 40
#> 8 1993:1:DEN:DAN2:GRT:112:51 1993 32
#> 9 1993:1:DEN:DAN2:GRT:113:52 1993 12
#> 10 1993:1:DEN:DAN2:GRT:114:53 1993 16
#> # … with 10,258 more rows
hlcodL %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, zero_catch) %>%
summarise(n = n()) %>%
ggplot(., aes(x = Year, y = n, fill = zero_catch)) +
geom_bar(stat = "identity")
# Flounder
hlfle0 <- hlfle0 %>%
mutate(CPUEun = ifelse(SpecVal == "1" & DataType == "C",
HLNoAtLngt,
ifelse(SpecVal == "1" & DataType == "R",
HLNoAtLngt/(HaulDur/60),
ifelse(SpecVal == "1" & DataType == "S",
(HLNoAtLngt*SubFactor)/(HaulDur/60),
ifelse(SpecVal == "zeroCatch", 0, NA)))))
# Sum up the CPUES if multiple per length class and haul
hlfleL <- hlfle0 %>%
group_by(LngtClass, haul.id) %>%
mutate(CPUEun = sum(CPUEun)) %>%
ungroup() %>%
mutate(id3 = paste(haul.id, LngtClass)) %>%
distinct(id3, .keep_all = TRUE) %>%
dplyr::select(-X, -id3)
hlfleL %>%
group_by(haul.id, Year) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
filter(!CPUEun_haul == 0)
#> # A tibble: 10,013 × 3
#> haul.id Year CPUEun_haul
#> <chr> <int> <dbl>
#> 1 1993:1:DEN:DAN2:GRT:105:45 1993 2500
#> 2 1993:1:DEN:DAN2:GRT:106:46 1993 8
#> 3 1993:1:DEN:DAN2:GRT:107:47 1993 26
#> 4 1993:1:DEN:DAN2:GRT:108:48 1993 34
#> 5 1993:1:DEN:DAN2:GRT:109:49 1993 8
#> 6 1993:1:DEN:DAN2:GRT:11:6 1993 11
#> 7 1993:1:DEN:DAN2:GRT:110:50 1993 8
#> 8 1993:1:DEN:DAN2:GRT:114:53 1993 1
#> 9 1993:1:DEN:DAN2:GRT:115:54 1993 3
#> 10 1993:1:DEN:DAN2:GRT:117:56 1993 729
#> # … with 10,003 more rowsGet and add annual weight-length relationships from the CA data for both cod and flounder so that I can calculate CPUE in biomass rather than numbers further down
# Cod
bits_ca_cod <- bits_ca %>%
filter(SpecCode %in% c("164712", "126436")) %>%
mutate(StNo = as.numeric(StNo)) %>%
mutate(Species = "Cod") %>%
mutate(ID = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
# Now I need to copy rows with NoAtLngt > 1 so that 1 row = 1 ind
# First make a small test
# nrow(bits_ca_cod)
# test_id <- head(filter(bits_ca_cod, CANoAtLngt == 5))$ID[1]
# filter(bits_ca_cod, ID == test_id & CANoAtLngt == 5)
bits_ca_cod <- bits_ca_cod %>% map_df(., rep, .$CANoAtLngt)
# head(data.frame(filter(bits_ca_cod, ID == test_id & CANoAtLngt == 5)), 20)
# nrow(bits_ca_cod)
# Looks ok!
# Standardize length and drop NA weights (need that for condition)
bits_ca_cod <- bits_ca_cod %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) # Standardize length ((https://vocab.ices.dk/?ref=18))
# Plot
ggplot(bits_ca_cod, aes(IndWgt, length_cm)) +
geom_point() +
facet_wrap(~Year)
# Now extract the coefficients for each year (not bothering with outliers at the moment)
cod_intercept <- bits_ca_cod %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "(Intercept)") %>%
mutate(a = exp(estimate)) %>%
mutate(Year = as.integer(Year)) %>%
dplyr::select(Year, a)
cod_slope <- bits_ca_cod %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "log(length_cm)") %>%
mutate(Year = as.integer(Year)) %>%
rename("b" = "estimate") %>%
dplyr::select(Year, b)
# Flounder
bits_ca_fle <- bits_ca %>%
filter(SpecCode %in% c("127141", "172894")) %>%
mutate(StNo = as.numeric(StNo)) %>%
mutate(Species = "Flounder") %>%
mutate(ID = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
bits_ca_fle <- bits_ca_fle %>% map_df(., rep, .$CANoAtLngt)
# Standardize length and drop NA weights (need that for condition)
bits_ca_fle <- bits_ca_fle %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) %>% # Standardize length ((https://vocab.ices.dk/?ref=18))
mutate(keep = ifelse(LngtCode == "." & Year == 2008, "N", "Y")) %>%
filter(keep == "Y") %>%
filter(length_cm < 70)
# Plot
ggplot(bits_ca_fle, aes(IndWgt, length_cm, color = LngtCode)) +
geom_point() +
facet_wrap(~Year)
# Now extract the coefficients for each year (not bothering with outliers at the moment)
fle_intercept <- bits_ca_fle %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "(Intercept)") %>%
mutate(a = exp(estimate)) %>%
mutate(Year = as.integer(Year)) %>%
dplyr::select(Year, a)
fle_slope <- bits_ca_fle %>%
split(.$Year) %>%
purrr::map(~lm(log(IndWgt) ~ log(length_cm), data = .x)) %>%
purrr::map_df(broom::tidy, .id = 'Year') %>%
filter(term == "log(length_cm)") %>%
mutate(Year = as.integer(Year)) %>%
rename("b" = "estimate") %>%
dplyr::select(Year, b)Join the annual L-W relationships to the respective catch data to calculate CPUE in biomass not abundance
# These are the haul-data
# hlcodL
# hlfleL
hlcodL <- left_join(hlcodL, cod_intercept, by = "Year")
hlcodL <- left_join(hlcodL, cod_slope, by = "Year")
hlfleL <- left_join(hlfleL, fle_intercept, by = "Year")
hlfleL <- left_join(hlfleL, fle_slope, by = "Year")Convert from CPUE in numbers to kg
# First standardize length to cm and then check how zero-catches are implemented at this stage
hlcodL <- hlcodL %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) # Standardize length ((https://vocab.ices.dk/?ref=18))
filter(hlcodL, length_cm == 0) # No such thing
#> # A tibble: 0 × 51
#> # … with 51 variables: RecordType <chr>, Survey <chr>, Quarter <int>,
#> # Country <chr>, Ship <chr>, Gear <chr>, SweepLngt <int>, GearEx <chr>,
#> # DoorType <lgl>, StNo <chr>, HaulNo <int>, Year <int>, SpecCodeType <chr>,
#> # SpecCode <int>, SpecVal <fct>, Sex <chr>, TotalNo <dbl>,
#> # CatIdentifier <int>, NoMeas <int>, SubFactor <dbl>, SubWgt <int>,
#> # CatCatchWgt <int>, LngtCode <chr>, LngtClass <int>, HLNoAtLngt <dbl>,
#> # DevStage <chr>, LenMeasType <int>, DateofCalculation <int>, …
# Now check if all rows where length is NA are the ones with zero catch!
hlcodL %>%
mutate(length2 = replace_na(length_cm, -9),
no_length = ifelse(length2 < 0, "T", "F")) %>%
ggplot(., aes(length2, CPUEun, color = no_length)) + geom_point(alpha = 0.2) + facet_wrap(~no_length)
#> Warning: Removed 1 rows containing missing values (geom_point).
hlcodL %>% filter(CPUEun == 0) %>% distinct(length_cm)
#> # A tibble: 1 × 1
#> length_cm
#> <dbl>
#> 1 NA
# Right, so all hauls with zero catch have NA length_cm. I don't have any NA catches
t <- hlcodL %>% drop_na(CPUEun)
t <- hlcodL %>% filter(CPUEun == 0)
t <- hlcodL %>% drop_na(length_cm)
# In other words, a zero catch is when the catch is zero and length_cm is NA
# In order to not get any NA CPUEs in unit biomass because length is NA (I want them instead
# to be 0, as the numbers-CPUE is), I will replace length_cm == NA with length_cm == 0 before
# calculating biomass CPUE
hlcodL <- hlcodL %>% mutate(length_cm2 = replace_na(length_cm, 0))
# Standardize length in the haul-data and calculate weight
hlcodL <- hlcodL %>%
mutate(weight_kg = (a*length_cm2^b)/1000) %>%
mutate(CPUEun_kg = weight_kg*CPUEun)
# Plot and check it's correct also in this data
ggplot(hlcodL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
# Hmm, some unrealistic weights actually
hlcodL %>% arrange(desc(weight_kg)) %>% as.data.frame() %>% head(50)
#> RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType StNo
#> 1 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 2 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 3 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 4 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 5 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 6 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 7 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 8 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 9 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 10 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 11 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 12 HL BITS 1 DEN 26D4 GRT NA <NA> NA 45
#> 13 HL BITS 1 SWE 77AR FOT 185 <NA> NA 80
#> 14 HL BITS 1 DEN 26D4 GRT NA S NA 83
#> 15 HL BITS 1 DEN 26D4 GRT 60 <NA> NA 43
#> 16 HL BITS 1 DEN 26D4 GRT NA <NA> NA 9
#> 17 HL BITS 1 SWE 77AR FOT 225 <NA> NA 191
#> 18 HL BITS 1 DEN 26D4 TVL 75 S NA 71
#> 19 HL BITS 1 GFR 06S1 H20 NA <NA> NA 46
#> 20 HL BITS 1 DEN 26D4 GRT 60 <NA> NA 117
#> 21 HL BITS 4 SWE 77AR GOV 75 <NA> NA 584
#> 22 HL BITS 1 DEN 26D4 GRT NA <NA> NA 75
#> 23 HL BITS 4 DEN 26D4 TVL 75 S NA 10
#> 24 HL BITS 1 DEN 26D4 GRT NA S NA 136
#> 25 HL BITS 4 SWE 77AR TVL 75 <NA> NA 573
#> 26 HL BITS 1 SWE 77AR GOV 100 <NA> NA 220
#> 27 HL BITS 1 SWE 77AR GOV 100 <NA> NA 237
#> 28 HL BITS 1 DEN 26D4 GRT NA <NA> NA 111
#> 29 HL BITS 4 SWE 77AR FOT 225 <NA> NA 231
#> 30 HL BITS 4 SWE 77AR FOT 203 <NA> NA 247
#> 31 HL BITS 1 SWE 77AR FOT 225 <NA> NA 207
#> 32 HL BITS 4 SWE 77AR GOV 100 <NA> NA 652
#> 33 HL BITS 1 DEN 26D4 GRT NA <NA> NA 53
#> 34 HL BITS 1 DEN 26D4 GRT NA <NA> NA 57
#> 35 HL BITS 1 RUS RUJB TVL NA <NA> NA <NA>
#> 36 HL BITS 1 GFR 06SL TVS NA <NA> NA 24316
#> 37 HL BITS 1 DEN 26D4 GRT 110 <NA> NA 162
#> 38 HL BITS 1 DEN 26D4 GRT 60 <NA> NA 74
#> 39 HL BITS 1 SWE 77AR FOT 225 <NA> NA 222
#> 40 HL BITS 1 GFR 06S1 H20 NA <NA> NA 44
#> 41 HL BITS 1 DEN 26D4 TVL NA S TRUE 95
#> 42 HL BITS 1 DEN 26D4 TVL 75 S NA 6
#> 43 HL BITS 1 SWE 77AR FOT 185 <NA> NA 58
#> 44 HL BITS 4 POL 67BC TVL 75 S NA 26132
#> 45 HL BITS 1 RUS RUJB HAK NA <NA> NA <NA>
#> 46 HL BITS 1 RUS RUJB HAK NA <NA> NA <NA>
#> 47 HL BITS 1 DEN 26D4 TVL NA S TRUE 19
#> 48 HL BITS 1 DEN 26D4 GRT 60 <NA> NA 17
#> 49 HL BITS 4 SWE 77AR FOT 180 <NA> NA 252
#> 50 HL BITS 4 DEN 26D4 TVL 75 S NA 94
#> HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier NoMeas
#> 1 40 1998 T 164712 1 <NA> 17 1 17
#> 2 40 1998 T 164712 1 <NA> 17 1 17
#> 3 40 1998 T 164712 1 <NA> 17 1 17
#> 4 40 1998 T 164712 1 <NA> 17 1 17
#> 5 40 1998 T 164712 1 <NA> 17 1 17
#> 6 40 1998 T 164712 1 <NA> 17 1 17
#> 7 40 1998 T 164712 1 <NA> 17 1 17
#> 8 40 1998 T 164712 1 <NA> 17 1 17
#> 9 40 1998 T 164712 1 <NA> 17 1 17
#> 10 40 1998 T 164712 1 <NA> 17 1 17
#> 11 40 1998 T 164712 1 <NA> 17 1 17
#> 12 25 1996 W 126436 1 <NA> 93 1 93
#> 13 32 1993 W 126436 1 <NA> 454 1 454
#> 14 40 1998 T 164712 1 <NA> 17 1 17
#> 15 22 1994 T 164712 1 <NA> 37 1 37
#> 16 5 1996 W 126436 1 <NA> 406 1 406
#> 17 3 1998 T 164712 1 <NA> 546 1 273
#> 18 36 2003 T 164712 1 <NA> 255 1 255
#> 19 20 1993 W 126436 1 <NA> 326 1 163
#> 20 50 1994 T 164712 1 <NA> 775 1 775
#> 21 18 2000 W 126436 1 <NA> 454 1 227
#> 22 40 1996 W 126436 1 <NA> 29 1 29
#> 23 4 2002 T 164712 1 <NA> 1958 1 1958
#> 24 64 1998 T 164712 1 <NA> 130 1 130
#> 25 11 2002 W 126436 1 <NA> 3090 1 1545
#> 26 21 2000 W 126436 1 <NA> 1076 1 538
#> 27 34 2000 W 126436 1 <NA> 638 1 319
#> 28 47 1996 W 126436 1 <NA> 78 1 78
#> 29 26 1996 W 126436 1 <NA> 54 1 27
#> 30 2 1995 W 126436 1 <NA> 2736 1 1368
#> 31 15 1998 T 164712 1 <NA> 20 1 10
#> 32 17 1999 T 164712 1 <NA> 1484 1 742
#> 33 30 1996 W 126436 1 <NA> 197 1 197
#> 34 23 1995 W 126436 1 <NA> 223 1 223
#> 35 13 2006 W 126436 1 <NA> 24 1 12
#> 36 41 2008 T 164712 1 <NA> 864 1 864
#> 37 72 1993 W 126436 1 <NA> 128 1 128
#> 38 39 1994 T 164712 1 <NA> 457 1 457
#> 39 26 1998 T 164712 1 <NA> 126 1 63
#> 40 24 1994 T 164712 1 <NA> 260 1 130
#> 41 66 2005 W 126436 1 <NA> 764 1 764
#> 42 3 2001 T 164712 1 <NA> 132 1 132
#> 43 10 1993 W 126436 1 <NA> 85 1 85
#> 44 24 2008 T 164712 1 <NA> 1696 1 848
#> 45 35 1998 T 164712 1 <NA> 1214 1 390
#> 46 33 1998 T 164712 1 <NA> 302 1 151
#> 47 17 2009 W 126436 1 <NA> 972 1 972
#> 48 9 1993 W 126436 1 <NA> 281 1 277
#> 49 21 1993 W 126436 1 <NA> 5932 1 1499
#> 50 46 2000 T 164712 1 <NA> 180 1 180
#> SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 1 NA 13275 0 335 1 <NA>
#> 2 1 NA 13275 0 285 1 <NA>
#> 3 1 NA 13275 0 225 2 <NA>
#> 4 1 NA 13275 0 220 1 <NA>
#> 5 1 NA 13275 0 215 1 <NA>
#> 6 1 NA 13275 0 180 1 <NA>
#> 7 1 NA 13275 0 175 1 <NA>
#> 8 1 NA 13275 0 160 1 <NA>
#> 9 1 NA 13275 0 150 2 <NA>
#> 10 1 NA 13275 0 145 3 <NA>
#> 11 1 NA 13275 0 140 1 <NA>
#> 12 1 113900 113900 . 1360 1 <NA>
#> 13 1 NA 234500 1 127 1 <NA>
#> 14 1 NA 13275 0 130 2 <NA>
#> 15 1 NA 1033 1 127 1 <NA>
#> 16 1 257280 257280 . 1230 1 <NA>
#> 17 1 NA 484399 1 121 2 <NA>
#> 18 1 NA 158200 1 121 1 <NA>
#> 19 1 NA 2190 1 116 2 <NA>
#> 20 1 NA 5718 1 118 1 <NA>
#> 21 1 NA 268580 1 118 2 <NA>
#> 22 1 48300 48300 . 1170 1 <NA>
#> 23 1 NA 1040200 1 118 1 <NA>
#> 24 1 NA 157570 1 116 1 <NA>
#> 25 1 NA 1302400 1 117 2 <NA>
#> 26 1 NA 303000 1 116 2 <NA>
#> 27 1 NA 158200 1 115 2 <NA>
#> 28 1 119000 119000 . 1130 1 <NA>
#> 29 1 NA 96200 1 113 2 <NA>
#> 30 1 NA 607800 1 111 2 <NA>
#> 31 1 NA 76000 1 113 2 <NA>
#> 32 1 NA 267459 1 113 2 <NA>
#> 33 1 192900 192900 . 1120 1 <NA>
#> 34 1 149100 149100 . 1100 1 <NA>
#> 35 1 NA 39544 1 118 2 <NA>
#> 36 1 NA 247830 1 119 1 <NA>
#> 37 1 NA 1079 1 109 1 <NA>
#> 38 1 NA 42 1 112 1 <NA>
#> 39 1 NA 48400 1 111 2 <NA>
#> 40 1 NA 2374 1 111 2 <NA>
#> 41 1 139400 139400 . 1150 1 <NA>
#> 42 1 NA 61000 1 111 1 <NA>
#> 43 1 NA 46000 1 107 1 <NA>
#> 44 1 NA 968860 1 116 2 <NA>
#> 45 1 NA 16278 1 109 3 <NA>
#> 46 1 NA 2962 1 109 2 <NA>
#> 47 1 322476 322476 . 1130 1 <NA>
#> 48 1 NA 1297 1 106 1 <NA>
#> 49 1 NA 5688600 1 106 3 <NA>
#> 50 1 NA 46605 1 109 1 <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20140617 126436 DAN2 DAN2
#> 2 NA 20140617 126436 DAN2 DAN2
#> 3 NA 20140617 126436 DAN2 DAN2
#> 4 NA 20140617 126436 DAN2 DAN2
#> 5 NA 20140617 126436 DAN2 DAN2
#> 6 NA 20140617 126436 DAN2 DAN2
#> 7 NA 20140617 126436 DAN2 DAN2
#> 8 NA 20140617 126436 DAN2 DAN2
#> 9 NA 20140617 126436 DAN2 DAN2
#> 10 NA 20140617 126436 DAN2 DAN2
#> 11 NA 20140617 126436 DAN2 DAN2
#> 12 NA 20190208 126436 DAN2 DAN2
#> 13 NA 20211203 126436 ARG ARG
#> 14 NA 20140617 126436 DAN2 DAN2
#> 15 NA 20161213 126436 DAN2 DAN2
#> 16 NA 20190208 126436 DAN2 DAN2
#> 17 NA 20140617 126436 ARG ARG
#> 18 NA 20131108 126436 DAN2 DAN2
#> 19 NA 20211203 126436 SOL SOL
#> 20 NA 20161213 126436 DAN2 DAN2
#> 21 NA 20131112 126436 ARG ARG
#> 22 NA 20190208 126436 DAN2 DAN2
#> 23 NA 20131113 126436 DAN2 DAN2
#> 24 NA 20140617 126436 DAN2 DAN2
#> 25 NA 20131113 126436 ARG ARG
#> 26 NA 20190228 126436 ARG ARG
#> 27 NA 20190228 126436 ARG ARG
#> 28 NA 20190208 126436 DAN2 DAN2
#> 29 NA 20161115 126436 ARG ARG
#> 30 NA 20161115 126436 ARG ARG
#> 31 NA 20140617 126436 ARG ARG
#> 32 NA 20131112 126436 ARG ARG
#> 33 NA 20190208 126436 DAN2 DAN2
#> 34 NA 20190207 126436 DAN2 DAN2
#> 35 NA 20200115 126436 ATLD ATLD
#> 36 NA 20180423 126436 SOL2 SOL2
#> 37 NA 20211203 126436 DAN2 DAN2
#> 38 NA 20161213 126436 DAN2 DAN2
#> 39 NA 20140617 126436 ARG ARG
#> 40 NA 20161213 126436 SOL SOL
#> 41 NA 20131204 126436 DAN2 DAN2
#> 42 NA 20160714 126436 DAN2 DAN2
#> 43 NA 20211203 126436 ARG ARG
#> 44 NA 20180507 126436 BAL BAL
#> 45 NA 20140617 126436 ATLD ATLD
#> 46 NA 20140617 126436 ATLD ATLD
#> 47 NA 20131216 126436 DAN2 DAN2
#> 48 NA 20211203 126436 DAN2 DAN2
#> 49 NA 20131111 126436 ARG ARG
#> 50 NA 20131112 126436 DAN2 DAN2
#> IDx sub_div Rect HaulVal StdSpecRecCode
#> 1 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 2 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 3 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 4 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 5 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 6 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 7 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 8 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 9 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 10 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 11 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 12 1996.1.DEN.26D4.GRT.45.25 26 41H0 V 1
#> 13 1993.1.SWE.77AR.FOT.80.32 25 39G5 V 1
#> 14 1998.1.DEN.26D4.GRT.83.40 25 38G5 V 1
#> 15 1994.1.DEN.26D4.GRT.43.22 26 41H0 V 1
#> 16 1996.1.DEN.26D4.GRT.9.5 25 38G5 V 1
#> 17 1998.1.SWE.77AR.FOT.191.3 25 40G5 V 1
#> 18 2003.1.DEN.26D4.TVL.71.36 25 39G5 V 1
#> 19 1993.1.GFR.06S1.H20.46.20 24 39G4 V 1
#> 20 1994.1.DEN.26D4.GRT.117.50 25 38G5 V 1
#> 21 2000.4.SWE.77AR.GOV.584.18 25 40G5 V 1
#> 22 1996.1.DEN.26D4.GRT.75.40 25 39G6 V 1
#> 23 2002.4.DEN.26D4.TVL.10.4 24 39G4 V 1
#> 24 1998.1.DEN.26D4.GRT.136.64 26 39G8 V 1
#> 25 2002.4.SWE.77AR.TVL.573.11 25 41G7 V 1
#> 26 2000.1.SWE.77AR.GOV.220.21 24 39G3 V 1
#> 27 2000.1.SWE.77AR.GOV.237.34 25 40G5 V 1
#> 28 1996.1.DEN.26D4.GRT.111.47 25 39G5 V 1
#> 29 1996.4.SWE.77AR.FOT.231.26 28 43G8 V 1
#> 30 1995.4.SWE.77AR.FOT.247.2 25 40G5 V 1
#> 31 1998.1.SWE.77AR.FOT.207.15 25 40G7 V 1
#> 32 1999.4.SWE.77AR.GOV.652.17 25 40G5 V 1
#> 33 1996.1.DEN.26D4.GRT.53.30 26 41G9 V 1
#> 34 1995.1.DEN.26D4.GRT.57.23 26 41G9 V 1
#> 35 2006.1.RUS.RUJB.TVL.NA.13 26 39H0 V 1
#> 36 2008.1.GFR.06SL.TVS.24316.41 24 39G4 V 1
#> 37 1993.1.DEN.26D4.GRT.162.72 25 38G5 V 1
#> 38 1994.1.DEN.26D4.GRT.74.39 26 41G8 V 1
#> 39 1998.1.SWE.77AR.FOT.222.26 28 43H0 V 1
#> 40 1994.1.GFR.06S1.H20.44.24 24 39G3 V 1
#> 41 2005.1.DEN.26D4.TVL.95.66 25 39G5 V 1
#> 42 2001.1.DEN.26D4.TVL.6.3 24 39G3 V 1
#> 43 1993.1.SWE.77AR.FOT.58.10 25 41G7 V 1
#> 44 2008.4.POL.67BC.TVL.26132.24 26 38G8 V 3
#> 45 1998.1.RUS.RUJB.HAK.NA.35 26 40G9 V 1
#> 46 1998.1.RUS.RUJB.HAK.NA.33 26 40G9 V 1
#> 47 2009.1.DEN.26D4.TVL.19.17 24 39G4 V 1
#> 48 1993.1.DEN.26D4.GRT.17.9 24 39G4 V 1
#> 49 1993.4.SWE.77AR.FOT.252.21 25 41G7 V 1
#> 50 2000.4.DEN.26D4.TVL.94.46 25 39G6 V 1
#> BySpecRecCode Fishing.line Month DataType HaulDur GroundSpeed
#> 1 1 -9.00 3 R 60 3.0
#> 2 1 -9.00 3 R 60 3.0
#> 3 1 -9.00 3 R 60 3.0
#> 4 1 -9.00 3 R 60 3.0
#> 5 1 -9.00 3 R 60 3.0
#> 6 1 -9.00 3 R 60 3.0
#> 7 1 -9.00 3 R 60 3.0
#> 8 1 -9.00 3 R 60 3.0
#> 9 1 -9.00 3 R 60 3.0
#> 10 1 -9.00 3 R 60 3.0
#> 11 1 -9.00 3 R 60 3.0
#> 12 1 -9.00 2 R 60 3.4
#> 13 1 83.00 3 C 60 3.0
#> 14 1 -9.00 3 R 60 3.0
#> 15 1 -9.00 3 C 60 -9.0
#> 16 1 -9.00 2 R 60 3.6
#> 17 1 83.00 2 C 30 3.3
#> 18 1 63.46 3 R 30 3.0
#> 19 1 36.00 2 C 30 4.0
#> 20 1 -9.00 3 C 60 -9.0
#> 21 1 160.00 11 C 30 3.7
#> 22 1 -9.00 3 R 60 3.2
#> 23 1 63.46 11 R 30 3.1
#> 24 1 -9.00 3 R 60 3.3
#> 25 1 63.46 11 C 30 3.0
#> 26 1 160.00 3 C 30 3.5
#> 27 1 160.00 3 C 30 3.5
#> 28 1 -9.00 3 R 60 3.5
#> 29 1 83.00 12 C 30 3.3
#> 30 1 83.00 12 C 30 3.7
#> 31 1 83.00 2 C 30 3.3
#> 32 1 160.00 11 C 30 3.3
#> 33 1 -9.00 3 R 60 3.1
#> 34 1 -9.00 2 R 60 -9.0
#> 35 1 63.46 3 C 30 3.0
#> 36 1 33.22 3 R 30 3.2
#> 37 1 -9.00 3 C 60 -9.0
#> 38 1 -9.00 3 C 60 -9.0
#> 39 1 83.00 3 C 30 3.4
#> 40 1 36.00 2 C 30 3.8
#> 41 1 63.46 3 R 30 3.0
#> 42 1 63.46 2 R 30 3.0
#> 43 1 83.00 3 C 60 3.5
#> 44 0 63.46 11 C 30 3.0
#> 45 1 -9.00 3 C 30 3.6
#> 46 1 -9.00 3 C 30 3.6
#> 47 1 63.46 3 R 30 2.9
#> 48 1 -9.00 3 C 59 -9.0
#> 49 1 83.00 11 C 30 3.0
#> 50 1 63.46 11 R 31 2.9
#> haul.id ShootLat ShootLong id_haul_stomach
#> 1 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 2 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 3 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 4 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 5 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 6 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 7 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 8 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 9 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 10 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 11 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 12 1996:1:DEN:DAN2:GRT:45:25 56.3700 20.0666 1996.1.2.DEN.41H0.25
#> 13 1993:1:SWE:ARG:FOT:80:32 55.4120 15.3205 1993.1.3.SWE.39G5.32
#> 14 1998:1:DEN:DAN2:GRT:83:40 54.8150 15.3217 1998.1.3.DEN.38G5.40
#> 15 1994:1:DEN:DAN2:GRT:43:22 56.4500 20.2000 1994.1.3.DEN.41H0.22
#> 16 1996:1:DEN:DAN2:GRT:9:5 54.5750 15.3200 1996.1.2.DEN.38G5.5
#> 17 1998:1:SWE:ARG:FOT:191:3 55.8218 15.4220 1998.1.2.SWE.40G5.3
#> 18 2003:1:DEN:DAN2:TVL:71:36 55.3709 15.5140 2003.1.3.DEN.39G5.36
#> 19 1993:1:GFR:SOL:H20:46:20 55.1333 14.1667 1993.1.2.GFR.39G4.20
#> 20 1994:1:DEN:DAN2:GRT:117:50 54.7833 15.9500 1994.1.3.DEN.38G5.50
#> 21 2000:4:SWE:ARG:GOV:584:18 55.8150 15.3917 2000.4.11.SWE.40G5.18
#> 22 1996:1:DEN:DAN2:GRT:75:40 55.1983 16.4916 1996.1.3.DEN.39G6.40
#> 23 2002:4:DEN:DAN2:TVL:10:4 55.4908 14.6121 2002.4.11.DEN.39G4.4
#> 24 1998:1:DEN:DAN2:GRT:136:64 55.4800 18.4683 1998.1.3.DEN.39G8.64
#> 25 2002:4:SWE:ARG:TVL:573:11 56.1460 17.7615 2002.4.11.SWE.41G7.11
#> 26 2000:1:SWE:ARG:GOV:220:21 55.0100 13.9300 2000.1.3.SWE.39G3.21
#> 27 2000:1:SWE:ARG:GOV:237:34 55.8467 15.5717 2000.1.3.SWE.40G5.34
#> 28 1996:1:DEN:DAN2:GRT:111:47 55.2150 15.6083 1996.1.3.DEN.39G5.47
#> 29 1996:4:SWE:ARG:FOT:231:26 57.0833 18.9000 1996.4.12.SWE.43G8.26
#> 30 1995:4:SWE:ARG:FOT:247:2 55.8323 15.5427 1995.4.12.SWE.40G5.2
#> 31 1998:1:SWE:ARG:FOT:207:15 55.9271 17.1108 1998.1.2.SWE.40G7.15
#> 32 1999:4:SWE:ARG:GOV:652:17 55.8400 15.5600 1999.4.11.SWE.40G5.17
#> 33 1996:1:DEN:DAN2:GRT:53:30 56.2566 19.5666 1996.1.3.DEN.41G9.30
#> 34 1995:1:DEN:DAN2:GRT:57:23 56.3666 19.8666 1995.1.2.DEN.41G9.23
#> 35 2006:1:RUS:ATLD:TVL:NA:13 55.1583 20.0267 2006.1.3.RUS.39H0.13
#> 36 2008:1:GFR:SOL2:TVS:24316:41 55.1505 14.0718 2008.1.3.GFR.39G4.41
#> 37 1993:1:DEN:DAN2:GRT:162:72 54.8500 15.6667 1993.1.3.DEN.38G5.72
#> 38 1994:1:DEN:DAN2:GRT:74:39 56.1167 18.2667 1994.1.3.DEN.41G8.39
#> 39 1998:1:SWE:ARG:FOT:222:26 57.2520 20.7436 1998.1.3.SWE.43H0.26
#> 40 1994:1:GFR:SOL:H20:44:24 55.0333 13.4333 1994.1.2.GFR.39G3.24
#> 41 2005:1:DEN:DAN2:TVL:95:66 55.0966 15.2680 2005.1.3.DEN.39G5.66
#> 42 2001:1:DEN:DAN2:TVL:6:3 55.0214 13.7642 2001.1.2.DEN.39G3.3
#> 43 1993:1:SWE:ARG:FOT:58:10 56.0352 17.7188 1993.1.3.SWE.41G7.10
#> 44 2008:4:POL:BAL:TVL:26132:24 54.5400 18.8867 2008.4.11.POL.38G8.24
#> 45 1998:1:RUS:ATLD:HAK:NA:35 55.5167 19.8833 1998.1.3.RUS.40G9.35
#> 46 1998:1:RUS:ATLD:HAK:NA:33 55.6167 19.6500 1998.1.3.RUS.40G9.33
#> 47 2009:1:DEN:DAN2:TVL:19:17 55.3195 14.9859 2009.1.3.DEN.39G4.17
#> 48 1993:1:DEN:DAN2:GRT:17:9 55.0333 14.2167 1993.1.3.DEN.39G4.9
#> 49 1993:4:SWE:ARG:FOT:252:21 56.1133 17.5917 1993.4.11.SWE.41G7.21
#> 50 2000:4:DEN:DAN2:TVL:94:46 55.1058 16.3850 2000.4.11.DEN.39G6.46
#> Species CPUEun a b length_cm length_cm2 weight_kg
#> 1 Gadus morhua 1.000000 0.007345618 3.082202 335 335 445.37563
#> 2 Gadus morhua 1.000000 0.007345618 3.082202 285 285 270.61760
#> 3 Gadus morhua 2.000000 0.007345618 3.082202 225 225 130.59602
#> 4 Gadus morhua 1.000000 0.007345618 3.082202 220 220 121.85635
#> 5 Gadus morhua 1.000000 0.007345618 3.082202 215 215 113.52063
#> 6 Gadus morhua 1.000000 0.007345618 3.082202 180 180 65.64985
#> 7 Gadus morhua 1.000000 0.007345618 3.082202 175 175 60.19004
#> 8 Gadus morhua 1.000000 0.007345618 3.082202 160 160 45.66372
#> 9 Gadus morhua 2.000000 0.007345618 3.082202 150 150 37.42667
#> 10 Gadus morhua 3.000000 0.007345618 3.082202 145 145 33.71329
#> 11 Gadus morhua 1.000000 0.007345618 3.082202 140 140 30.25718
#> 12 Gadus morhua 1.000000 0.008380383 3.055535 136 136 27.69283
#> 13 Gadus morhua 1.000000 0.007244481 3.100800 127 127 24.18146
#> 14 Gadus morhua 2.000000 0.007345618 3.082202 130 130 24.07846
#> 15 Gadus morhua 1.000000 0.008339286 3.052078 127 127 21.98379
#> 16 Gadus morhua 1.000000 0.008380383 3.055535 123 123 20.37242
#> 17 Gadus morhua 2.000000 0.007345618 3.082202 121 121 19.30161
#> 18 Gadus morhua 2.000000 0.007444717 3.070310 121 121 18.47755
#> 19 Gadus morhua 2.000000 0.007244481 3.100800 116 116 18.25909
#> 20 Gadus morhua 1.000000 0.008339286 3.052078 118 118 17.56609
#> 21 Gadus morhua 2.000000 0.007554303 3.071838 118 118 17.48561
#> 22 Gadus morhua 1.000000 0.008380383 3.055535 117 117 17.48553
#> 23 Gadus morhua 2.000000 0.007243730 3.076256 118 118 17.12383
#> 24 Gadus morhua 1.000000 0.007345618 3.082202 116 116 16.94747
#> 25 Gadus morhua 2.000000 0.007243730 3.076256 117 117 16.68132
#> 26 Gadus morhua 2.000000 0.007554303 3.071838 116 116 16.59110
#> 27 Gadus morhua 2.000000 0.007554303 3.071838 115 115 16.15566
#> 28 Gadus morhua 1.000000 0.008380383 3.055535 113 113 15.72235
#> 29 Gadus morhua 2.000000 0.008380383 3.055535 113 113 15.72235
#> 30 Gadus morhua 2.000000 0.007216770 3.098570 111 111 15.70067
#> 31 Gadus morhua 2.000000 0.007345618 3.082202 113 113 15.63258
#> 32 Gadus morhua 2.000000 0.006831731 3.097311 113 113 15.61542
#> 33 Gadus morhua 1.000000 0.008380383 3.055535 112 112 15.30107
#> 34 Gadus morhua 1.000000 0.007216770 3.098570 110 110 15.26652
#> 35 Gadus morhua 2.000000 0.009208543 3.001584 118 118 15.24467
#> 36 Gadus morhua 2.000000 0.009197813 2.994628 119 119 15.10690
#> 37 Gadus morhua 1.000000 0.007244481 3.100800 109 109 15.05428
#> 38 Gadus morhua 1.000000 0.008339286 3.052078 112 112 14.97969
#> 39 Gadus morhua 2.000000 0.007345618 3.082202 111 111 14.79541
#> 40 Gadus morhua 2.000000 0.008339286 3.052078 111 111 14.57521
#> 41 Gadus morhua 2.000000 0.008377248 3.027632 115 115 14.52564
#> 42 Gadus morhua 2.000000 0.007693063 3.064660 111 111 14.26658
#> 43 Gadus morhua 1.000000 0.007244481 3.100800 107 107 14.21416
#> 44 Gadus morhua 2.000000 0.009197813 2.994628 116 116 13.99484
#> 45 Gadus morhua 3.000000 0.007345618 3.082202 109 109 13.98905
#> 46 Gadus morhua 2.000000 0.007345618 3.082202 109 109 13.98905
#> 47 Gadus morhua 2.000000 0.007746254 3.044723 113 113 13.80850
#> 48 Gadus morhua 1.000000 0.007244481 3.100800 106 106 13.80627
#> 49 Gadus morhua 3.000000 0.007244481 3.100800 106 106 13.80627
#> 50 Gadus morhua 1.935484 0.007554303 3.071838 109 109 13.70373
#> CPUEun_kg
#> 1 445.37563
#> 2 270.61760
#> 3 261.19205
#> 4 121.85635
#> 5 113.52063
#> 6 65.64985
#> 7 60.19004
#> 8 45.66372
#> 9 74.85333
#> 10 101.13986
#> 11 30.25718
#> 12 27.69283
#> 13 24.18146
#> 14 48.15692
#> 15 21.98379
#> 16 20.37242
#> 17 38.60322
#> 18 36.95509
#> 19 36.51819
#> 20 17.56609
#> 21 34.97121
#> 22 17.48553
#> 23 34.24765
#> 24 16.94747
#> 25 33.36265
#> 26 33.18221
#> 27 32.31132
#> 28 15.72235
#> 29 31.44471
#> 30 31.40135
#> 31 31.26517
#> 32 31.23085
#> 33 15.30107
#> 34 15.26652
#> 35 30.48935
#> 36 30.21379
#> 37 15.05428
#> 38 14.97969
#> 39 29.59081
#> 40 29.15042
#> 41 29.05128
#> 42 28.53316
#> 43 14.21416
#> 44 27.98968
#> 45 41.96716
#> 46 27.97811
#> 47 27.61701
#> 48 13.80627
#> 49 41.41880
#> 50 26.52336
hlcodL <- hlcodL %>% filter(weight_kg < 100 & length_cm2 < 135)
ggplot(hlcodL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
# Now do the same for flounder
# First standardize length to cm and then check how zero-catches are implemented at this stage
hlfleL <- hlfleL %>%
mutate(length_cm = ifelse(LngtCode %in% c(".", "0"),
LngtClass/10,
LngtClass)) # Standardize length (https://vocab.ices.dk/?ref=18)
filter(hlfleL, length_cm == 0) # No such thing
#> # A tibble: 0 × 51
#> # … with 51 variables: RecordType <chr>, Survey <chr>, Quarter <int>,
#> # Country <chr>, Ship <chr>, Gear <chr>, SweepLngt <int>, GearEx <chr>,
#> # DoorType <lgl>, StNo <chr>, HaulNo <int>, Year <int>, SpecCodeType <chr>,
#> # SpecCode <int>, SpecVal <fct>, Sex <chr>, TotalNo <dbl>,
#> # CatIdentifier <int>, NoMeas <int>, SubFactor <dbl>, SubWgt <int>,
#> # CatCatchWgt <int>, LngtCode <chr>, LngtClass <int>, HLNoAtLngt <dbl>,
#> # DevStage <chr>, LenMeasType <int>, DateofCalculation <int>, …
bits_ca_fle <- bits_ca_fle %>%
drop_na(IndWgt) %>%
drop_na(LngtClass) %>%
filter(IndWgt > 0 & LngtClass > 0) %>% # Filter positive length and weight
mutate(weight_kg = IndWgt/1000) %>%
mutate(length_cm = ifelse(LngtCode == ".",
LngtClass/10,
LngtClass)) %>% # Standardize length ((https://vocab.ices.dk/?ref=18))
mutate(keep = ifelse(LngtCode == "." & Year == 2008, "N", "Y")) %>%
filter(keep == "Y") %>%
filter(length_cm < 70)
# Now check if all rows where length is NA are the ones with zero catch!
hlfleL %>%
mutate(length2 = replace_na(length_cm, -9),
no_length = ifelse(length2 < 0, "T", "F")) %>%
ggplot(., aes(length2, CPUEun, color = no_length)) + geom_point(alpha = 0.2) + facet_wrap(~no_length)
#> Warning: Removed 13 rows containing missing values (geom_point).
hlfleL %>% mutate(length2 = replace_na(length_cm, -9)) %>% group_by(length2) %>% distinct(CPUEun) %>% arrange(CPUEun)
#> # A tibble: 12,344 × 2
#> # Groups: length2 [253]
#> CPUEun length2
#> <dbl> <dbl>
#> 1 0 -9
#> 2 0.667 19
#> 3 0.667 21
#> 4 0.667 35
#> 5 0.667 39
#> 6 0.667 40
#> 7 0.667 42
#> 8 0.870 27
#> 9 0.870 32
#> 10 0.870 37.5
#> # … with 12,334 more rows
# Right, so all hauls with zero catch have NA length_cm. I don't have any NA catches
t <- hlfleL %>% drop_na(CPUEun)
# Well, 11 rows. I will remove them
hlfleL <- hlfleL %>% drop_na(CPUEun)
t <- hlfleL %>% filter(CPUEun == 0) %>% distinct(length_cm)
t <- hlfleL %>% drop_na(length_cm)
# In other words, a zero catch is when the catch is zero and length_cm is NA
# In order to not get any NA CPUEs in unit biomass because length is NA (I want them instead
# to be 0, as the numbers-CPUE is), I will replace length_cm == NA with length_cm == 0 before
# calculating biomass cpue
hlfleL <- hlfleL %>% mutate(length_cm2 = replace_na(length_cm, 0))
# Standardize length in the haul-data and calculate weight
hlfleL <- hlfleL %>%
mutate(weight_kg = (a*length_cm2^b)/1000) %>%
mutate(CPUEun_kg = weight_kg*CPUEun)
# Plot and check it's correct also in this data
ggplot(hlfleL, aes(weight_kg, length_cm2)) +
geom_point() +
facet_wrap(~Year)
# Check
t <- hlfleL %>% drop_na(CPUEun_kg) # Should not have any NA in biomass-catch
t <- hlfleL %>% filter(CPUEun_kg == 0) # Should result in a few percent of rows (note this is not proportion of hauls, but rows)
t <- hlfleL %>% drop_na(length_cm2) # Should be no NA
# What is the proportion of zero-catch hauls?
hlcodL %>%
group_by(haul.id) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
filter(CPUEun_haul == 0)
#> # A tibble: 1,985 × 2
#> haul.id CPUEun_haul
#> <chr> <dbl>
#> 1 1993:1:DEN:DAN2:GRT:1:1 0
#> 2 1993:1:DEN:DAN2:GRT:3:2 0
#> 3 1993:1:GFR:SOL:H20:60:42 0
#> 4 1993:1:LAT:LAIZ:LBT:1:1 0
#> 5 1993:1:LAT:LAIZ:LBT:1:10 0
#> 6 1993:1:LAT:LAIZ:LBT:1:11 0
#> 7 1993:1:LAT:LAIZ:LBT:1:12 0
#> 8 1993:1:LAT:LAIZ:LBT:1:13 0
#> 9 1993:1:LAT:LAIZ:LBT:1:14 0
#> 10 1993:1:LAT:LAIZ:LBT:1:15 0
#> # … with 1,975 more rows
cod_0plot <- hlcodL %>%
group_by(haul.id, Year, Quarter) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, Quarter, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_zero_catch_hauls = Y/(N+Y)) %>%
ggplot(., aes(Year, prop_zero_catch_hauls)) + geom_bar(stat = "identity") +
coord_cartesian(expand = 0, ylim = c(0, 1)) +
facet_wrap(~ Quarter) +
ggtitle("Cod")
# How many zero-catch hauls?
hlfleL %>%
group_by(haul.id) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
filter(CPUEun_haul == 0)
#> # A tibble: 1,990 × 2
#> haul.id CPUEun_haul
#> <chr> <dbl>
#> 1 1993:1:DEN:DAN2:GRT:1:1 0
#> 2 1993:1:DEN:DAN2:GRT:112:51 0
#> 3 1993:1:DEN:DAN2:GRT:113:52 0
#> 4 1993:1:DEN:DAN2:GRT:116:55 0
#> 5 1993:1:DEN:DAN2:GRT:136:62 0
#> 6 1993:1:DEN:DAN2:GRT:3:2 0
#> 7 1993:1:DEN:DAN2:GRT:54:25 0
#> 8 1993:1:DEN:DAN2:GRT:68:29 0
#> 9 1993:1:DEN:DAN2:GRT:70:30 0
#> 10 1993:1:GFR:SOL:H20:54:60 0
#> # … with 1,980 more rows
fle_0plot <- hlfleL %>%
group_by(haul.id, Year, Quarter) %>%
summarise(CPUEun_haul = sum(CPUEun)) %>%
ungroup() %>%
mutate(zero_catch = ifelse(CPUEun_haul == 0, "Y", "N")) %>%
group_by(Year, Quarter, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_zero_catch_hauls = Y/(N+Y)) %>%
ggplot(., aes(Year, prop_zero_catch_hauls)) + geom_bar(stat = "identity") +
coord_cartesian(expand = 0, ylim = c(0, 1)) +
facet_wrap(~ Quarter) +
ggtitle("Flounder")
cod_0plot / fle_0plot
#> Warning: Removed 12 rows containing missing values (position_stack).
#> Warning: Removed 12 rows containing missing values (position_stack).
Standardize according to Orio
To get unit: kg of fish caught by trawling for 1 h a standard bottom swept area of 0.45km2 using a TVL trawl with 75 m sweeps at the standard speed of three knots
# Remove hauls done with the TVL gear with a SweepLngt < 50 (these are calibration hauls, pers. com. Anders & Ale)
# And also hauls without length-information
# Remove pelagic gear
hlcodL <- hlcodL %>%
mutate(SweepLngt2 = replace_na(SweepLngt, 50)) %>%
mutate(keep = ifelse(Gear == "TVL" & SweepLngt2 < 50, "N", "Y")) %>%
filter(keep == "Y") %>%
dplyr::select(-keep, -SweepLngt2) %>%
filter(!Gear == "PEL")
hlfleL <- hlfleL %>%
mutate(SweepLngt2 = replace_na(SweepLngt, 50)) %>%
mutate(keep = ifelse(Gear == "TVL" & SweepLngt2 < 50, "N", "Y")) %>%
filter(keep == "Y") %>%
dplyr::select(-keep, -SweepLngt2) %>%
filter(!Gear == "PEL")
# Add in RS and RSA-values from the sweep file
# CPUE should be multiplied with RS and RSA to standardize to a relative speed and gear dimension.
# There is not a single file will all RS and RSA values. Instead they come in three files:
# - sweep (non-Swedish hauls between 1991-2016)
# - + calculated based on trawl speed and gear dimensions.
# I will join in the RS and RSA values from all sources, then standardize and filter
# away non-standardized hauls
# sort(unique(sweep$Year))
# sort(unique(sweep$Country))
# Since I don't have the sweep data for Swedish data, I have to calculate it from scratch using the
# equation in Orio's spreadsheet
# First I will join in the sweep data,
sweep_sel <- sweep %>% rename("haul.id" = "ï..haul.id") %>% dplyr::select(haul.id, RSA, RS)
hlcodL2 <- left_join(hlcodL, sweep_sel)
hlfleL2 <- left_join(hlfleL, sweep_sel)
hlcodL2 <- hlcodL2 %>%
rename("RS_sweep" = "RS",
"RSA_sweep" = "RSA") %>%
mutate(RS_sweep = as.numeric(RS_sweep),
RSA_sweep = as.numeric(RSA_sweep))
hlfleL2 <- hlfleL2 %>%
rename("RS_sweep" = "RS",
"RSA_sweep" = "RSA") %>%
mutate(RS_sweep = as.numeric(RS_sweep),
RSA_sweep = as.numeric(RSA_sweep))
sort(colnames(hlcodL2))
#> [1] "a" "b" "BySpecRecCode"
#> [4] "CatCatchWgt" "CatIdentifier" "Country"
#> [7] "CPUEun" "CPUEun_kg" "DataType"
#> [10] "DateofCalculation" "DevStage" "DoorType"
#> [13] "Fishing.line" "Gear" "GearEx"
#> [16] "GroundSpeed" "haul.id" "HaulDur"
#> [19] "HaulNo" "HaulVal" "HLNoAtLngt"
#> [22] "id_haul_stomach" "IDx" "length_cm"
#> [25] "length_cm2" "LenMeasType" "LngtClass"
#> [28] "LngtCode" "Month" "NoMeas"
#> [31] "Quarter" "RecordType" "Rect"
#> [34] "RS_sweep" "RSA_sweep" "Sex"
#> [37] "Ship" "Ship2" "Ship3"
#> [40] "ShootLat" "ShootLong" "SpecCode"
#> [43] "SpecCodeType" "Species" "SpecVal"
#> [46] "StdSpecRecCode" "StNo" "sub_div"
#> [49] "SubFactor" "SubWgt" "Survey"
#> [52] "SweepLngt" "TotalNo" "Valid_Aphia"
#> [55] "weight_kg" "Year"
sort(colnames(hlfleL2))
#> [1] "a" "b" "BySpecRecCode"
#> [4] "CatCatchWgt" "CatIdentifier" "Country"
#> [7] "CPUEun" "CPUEun_kg" "DataType"
#> [10] "DateofCalculation" "DevStage" "DoorType"
#> [13] "Fishing.line" "Gear" "GearEx"
#> [16] "GroundSpeed" "haul.id" "HaulDur"
#> [19] "HaulNo" "HaulVal" "HLNoAtLngt"
#> [22] "id_haul_stomach" "IDx" "length_cm"
#> [25] "length_cm2" "LenMeasType" "LngtClass"
#> [28] "LngtCode" "Month" "NoMeas"
#> [31] "Quarter" "RecordType" "Rect"
#> [34] "RS_sweep" "RSA_sweep" "Sex"
#> [37] "Ship" "Ship2" "Ship3"
#> [40] "ShootLat" "ShootLong" "SpecCode"
#> [43] "SpecCodeType" "Species" "SpecVal"
#> [46] "StdSpecRecCode" "StNo" "sub_div"
#> [49] "SubFactor" "SubWgt" "Survey"
#> [52] "SweepLngt" "TotalNo" "Valid_Aphia"
#> [55] "weight_kg" "Year"
# I will calculate a RS and RSA column in the catch data based on Ale's equation in the sweep file:
sort(unique(hlcodL2$GroundSpeed))
#> [1] -9.0 0.1 0.2 0.8 1.7 1.8 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8
#> [16] 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3
#> [31] 4.4 4.5 4.6 4.7 4.9 5.0 5.2 5.3 5.4 5.5 5.6 5.7 5.9 6.0 6.1
#> [46] 6.2 6.3 6.6 6.7 6.8 6.9 7.1 7.3 8.6
sort(unique(hlcodL2$Fishing.line))
#> [1] -9.00 28.00 33.22 36.00 39.80 63.46 83.00 160.00
sort(unique(hlcodL2$SweepLngt))
#> [1] 0 40 50 60 75 87 90 95 100 110 135 180 185 200 203 225 235
# First replace -9 in the columns I use for the calculations with NA so I don't end up with real numbers that are wrong!
hlcodL2 <- hlcodL2 %>% mutate(GroundSpeed = ifelse(GroundSpeed == -9, NA, GroundSpeed),
Fishing.line = ifelse(Fishing.line == -9, NA, Fishing.line),
SweepLngt = ifelse(SweepLngt == -9, NA, SweepLngt))
hlfleL2 <- hlfleL2 %>% mutate(GroundSpeed = ifelse(GroundSpeed == -9, NA, GroundSpeed),
Fishing.line = ifelse(Fishing.line == -9, NA, Fishing.line),
SweepLngt = ifelse(SweepLngt == -9, NA, SweepLngt))
hlcodL2 %>% filter(Quarter == 1) %>%
distinct(GroundSpeed, Fishing.line, SweepLngt) %>% as.data.frame() %>% head(50)
#> SweepLngt Fishing.line GroundSpeed
#> 1 60 NA NA
#> 2 110 NA NA
#> 3 NA 36.0 3.8
#> 4 NA 36.0 3.6
#> 5 NA 36.0 4.0
#> 6 NA 36.0 4.6
#> 7 NA 36.0 3.4
#> 8 NA 36.0 4.2
#> 9 NA 36.0 3.2
#> 10 NA 36.0 2.6
#> 11 NA 36.0 2.8
#> 12 50 160.0 3.1
#> 13 50 160.0 2.9
#> 14 50 160.0 3.0
#> 15 50 160.0 2.6
#> 16 50 160.0 3.2
#> 17 185 83.0 3.1
#> 18 185 83.0 3.0
#> 19 NA 39.8 NA
#> 20 185 83.0 2.8
#> 21 185 83.0 3.4
#> 22 185 83.0 3.3
#> 23 185 83.0 3.5
#> 24 185 83.0 3.2
#> 25 185 83.0 2.7
#> 26 185 83.0 2.9
#> 27 NA 36.0 4.4
#> 28 NA 36.0 3.0
#> 29 NA 36.0 2.4
#> 30 180 83.0 3.1
#> 31 180 83.0 3.2
#> 32 180 83.0 3.0
#> 33 180 83.0 3.3
#> 34 50 160.0 3.4
#> 35 180 83.0 3.4
#> 36 180 83.0 3.5
#> 37 180 83.0 2.8
#> 38 180 83.0 3.9
#> 39 50 160.0 3.3
#> 40 NA NA NA
#> 41 NA 36.0 3.7
#> 42 NA 36.0 3.1
#> 43 NA NA 3.2
#> 44 NA NA 3.3
#> 45 NA NA 3.6
#> 46 NA NA 3.4
#> 47 90 160.0 3.4
#> 48 NA NA 3.0
#> 49 NA NA 3.8
#> 50 NA NA 3.9
hlcodL2 %>% filter(Quarter == 4) %>%
distinct(GroundSpeed, Fishing.line, SweepLngt) %>% as.data.frame() %>% head(50)
#> SweepLngt Fishing.line GroundSpeed
#> 1 180 83 2.9
#> 2 NA 36 3.8
#> 3 180 83 2.8
#> 4 180 83 3.1
#> 5 NA 36 3.4
#> 6 NA 36 4.0
#> 7 NA 36 4.2
#> 8 180 83 3.2
#> 9 180 83 3.3
#> 10 NA 36 3.2
#> 11 NA 36 3.6
#> 12 180 83 3.0
#> 13 NA 36 3.0
#> 14 185 83 2.8
#> 15 185 83 3.0
#> 16 185 83 3.2
#> 17 185 83 3.3
#> 18 185 83 3.1
#> 19 185 83 3.5
#> 20 185 83 3.4
#> 21 185 83 3.6
#> 22 NA 36 NA
#> 23 203 83 3.6
#> 24 203 83 3.7
#> 25 203 83 3.5
#> 26 203 83 3.8
#> 27 203 83 3.9
#> 28 203 83 3.4
#> 29 100 160 3.4
#> 30 100 83 3.5
#> 31 100 83 3.4
#> 32 225 83 3.6
#> 33 225 83 3.5
#> 34 100 83 4.0
#> 35 100 160 3.5
#> 36 100 83 3.7
#> 37 100 83 3.3
#> 38 100 83 3.2
#> 39 100 83 3.6
#> 40 225 83 3.3
#> 41 225 83 3.4
#> 42 180 83 3.6
#> 43 NA 28 NA
#> 44 225 83 3.2
#> 45 180 83 3.4
#> 46 180 83 3.5
#> 47 225 83 3.7
#> 48 225 83 3.8
#> 49 185 83 3.8
#> 50 225 83 3.9
# Hmm, Q1 has at least one of the RS or RSA variables as NAs. Will be difficult to standardize!
# Hope the correction factors are present in Ales conversion data
# Now calculate correction factors
hlcodL2 <- hlcodL2 %>% mutate(RS_x = 3/GroundSpeed,
Horizontal.opening..m. = Fishing.line*0.67,
Swep.one.side..after.formula...meter = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m = Horizontal.opening..m. + (Swep.one.side..after.formula...meter*2),
Swept.area = (Size.final..m*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area)
hlfleL2 <- hlfleL2 %>% mutate(RS_x = 3/GroundSpeed,
Horizontal.opening..m. = Fishing.line*0.67,
Swep.one.side..after.formula...meter = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m = Horizontal.opening..m. + (Swep.one.side..after.formula...meter*2),
Swept.area = (Size.final..m*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area)
# Check EQ. is correct by recalculating it in the sweep file
sweep <- sweep %>% mutate(Horizontal.opening..m.2 = Fishing.line*0.67,
Swep.one.side..after.formula...meter2 = 0.258819045*SweepLngt, # SIN(RADIANS(15))
Size.final..m2 = Horizontal.opening..m.2 + (Swep.one.side..after.formula...meter2*2),
Swept.area2 = (Size.final..m2*3*1860)/1000000,
RSA_x = 0.45388309675081/Swept.area2)
sweep %>%
drop_na() %>%
ggplot(., aes(as.numeric(RSA), RSA_x)) + geom_point() + geom_abline(intercept = 0, slope = 1)
# Yes it's the same
# Replace NAs with -1/3 (because ICES codes missing values as -9 and in the calculation above they get -1/3),
# so that I can filter them easily later
# sort(unique(hlcodL2$RS_x))
# sort(unique(hlcodL2$RSA_x))
hlcodL2$RS_x[is.na(hlcodL2$RS_x)] <- -1/3
hlcodL2$RS_sweep[is.na(hlcodL2$RS_sweep)] <- -1/3
hlcodL2$RSA_x[is.na(hlcodL2$RSA_x)] <- -1/3
hlcodL2$RSA_sweep[is.na(hlcodL2$RSA_sweep)] <- -1/3
hlfleL2$RS_x[is.na(hlfleL2$RS_x)] <- -1/3
hlfleL2$RS_sweep[is.na(hlfleL2$RS_sweep)] <- -1/3
hlfleL2$RSA_x[is.na(hlfleL2$RSA_x)] <- -1/3
hlfleL2$RSA_sweep[is.na(hlfleL2$RSA_sweep)] <- -1/3
# Compare the difference correction factors (calculated vs imported from sweep file)
p1 <- ggplot(filter(hlcodL2, RS_x > 0), aes(RS_x)) + geom_histogram() + xlim(0.4, 1.76)
p2 <- ggplot(hlcodL2, aes(RSA_x)) + geom_histogram()
p3 <- ggplot(hlcodL2, aes(RS_sweep)) + geom_histogram()
p4 <- ggplot(hlcodL2, aes(RSA_sweep)) + geom_histogram()
(p1 + p2) / (p3 + p4)
#> Warning: Removed 284 rows containing non-finite values (stat_bin).
#> Warning: Removed 2 rows containing missing values (geom_bar).
p5 <- ggplot(filter(hlfleL2, RS_x > 0), aes(RS_x)) + geom_histogram() + xlim(0.4, 1.76)
p6 <- ggplot(hlfleL2, aes(RSA_x)) + geom_histogram()
p7 <- ggplot(hlfleL2, aes(RS_sweep)) + geom_histogram()
p8 <- ggplot(hlfleL2, aes(RSA_sweep)) + geom_histogram()
(p5 + p6) / (p7 + p8)
#> Warning: Removed 115 rows containing non-finite values (stat_bin).
#> Removed 2 rows containing missing values (geom_bar).
# Why do I have RSA values smaller than one? (either because sweep length is longer or gear is larger (GOV))
# Check if I can calculate the same RSA in sweep as that entered there.
# Ok, so the equation is correct. Which ID's have RSA < 1?
hlcodL2 %>%
filter(RSA_x < 1 & RSA_x > 0) %>%
dplyr::select(Year, Country, Ship, Gear, haul.id, Horizontal.opening..m., Fishing.line,
Swep.one.side..after.formula...meter, SweepLngt, Size.final..m, Swept.area, RSA_x) %>%
ggplot(., aes(RSA_x, fill = factor(SweepLngt))) + geom_histogram() + facet_wrap(~Gear, ncol = 1)
#> Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
#> Returning the palette you asked for with that many colors
# Check if I have more than one unique RS or RSA value per haul, or if it's "either this or that"
# Filter positive in both columns
hlcodL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlcodL2 %>% filter(RSA_x > 0 & RSA_sweep > 0) %>% ggplot(., aes(RSA_x, RSA_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RSA_x > 0 & RSA_sweep > 0) %>% ggplot(., aes(RSA_x, RSA_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
# Ok, there's on odd RS_x that is larger than 3. It didn't catch anything and speed is 0.8! Will remove
hlcodL2 <- hlcodL2 %>% filter(RS_x < 3)
hlfleL2 <- hlfleL2 %>% filter(RS_x < 3)
# Plot again
hlcodL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
hlfleL2 %>% filter(RS_x > 0 & RS_sweep > 0) %>% ggplot(., aes(RS_x, RS_sweep)) +
geom_point() + geom_abline(aes(intercept = 0, slope = 1), color = "red")
# They are largely the same when they overlap. When they differ, I will use RS_sweep
# Make a single RS and RSA column
# Cod
hlcodL3 <- hlcodL2 %>%
mutate(RS = -99,
RS = ifelse(RS_sweep > 0, RS_sweep, RS),
RS = ifelse(RS < 0 & RS_x > 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
mutate(RSA = -99,
RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
RSA = ifelse(RSA < 0 & RSA_x > 0, RSA_x, RSA)) %>%
filter(RS > 0) %>%
filter(RSA > 0) %>%
mutate(RSRSA = RS*RSA)
# Plot
ggplot(hlcodL3, aes(RSRSA)) + geom_histogram()
hlfleL2 %>% filter(Country == "LAT") %>% distinct(Year) %>% arrange(Year)
#> # A tibble: 28 × 1
#> Year
#> <int>
#> 1 1993
#> 2 1994
#> 3 1995
#> 4 1996
#> 5 1997
#> 6 1998
#> 7 1999
#> 8 2000
#> 9 2001
#> 10 2002
#> # … with 18 more rows
# Flounder
hlfleL3 <- hlfleL2 %>%
mutate(RS = -999,
RS = ifelse(RS_sweep > 0, RS_sweep, RS),
RS = ifelse(RS < 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
mutate(RSA = -999,
RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
RSA = ifelse(RSA < 0, RSA_x, RSA)) %>%
filter(RS > 0) %>%
filter(RSA > 0) %>%
mutate(RSRSA = RS*RSA)
# Test how many years of LAT data I miss out on because I can't standardize it.
# hlfleL2 %>%
# mutate(RS = -999,
# RS = ifelse(RS_sweep > 0, RS_sweep, RS),
# RS = ifelse(RS < 0, RS_x, RS)) %>% # Note that there are no NA i RS_x. This replaces all non-RS_sweep values -0.3, so I can filter positive later
# filter(RS > 0) %>%
# filter(Country == "LAT") %>%
# distinct(Year) %>%
# arrange(Year)
#
# hlfleL2 %>%
# mutate(RSA = -999,
# RSA = ifelse(RSA_sweep > 0, RSA_sweep, RSA),
# RSA = ifelse(RSA < 0, RSA_x, RSA)) %>%
# filter(RSA > 0) %>%
# filter(Country == "LAT") %>%
# distinct(Year) %>%
# arrange(Year)
# Plot
ggplot(hlcodL3, aes(RSRSA)) + geom_histogram()
# Standardize!
hlcodL3 <- hlcodL3 %>%
mutate(CPUEst_kg = CPUEun_kg*RS*RSA,
CPUEst = CPUEun*RS*RSA)
hlfleL3 <- hlfleL3 %>%
mutate(CPUEst_kg = CPUEun_kg*RS*RSA,
CPUEst = CPUEun*RS*RSA)
unique(is.na(hlcodL3$CPUEst_kg))
#> [1] FALSE
unique(is.na(hlcodL3$CPUEst))
#> [1] FALSE
min(hlcodL3$CPUEst_kg)
#> [1] 0
min(hlcodL3$CPUEst)
#> [1] 0
unique(is.na(hlfleL3$CPUEst_kg)) # Remove the few NA's here
#> [1] FALSE
hlfleL3 <- hlfleL3 %>% drop_na(CPUEst_kg)
unique(is.na(hlfleL3$CPUEst))
#> [1] FALSE
min(hlfleL3$CPUEst_kg)
#> [1] 0
min(hlfleL3$CPUEst)
#> [1] 0
# Now calculate CPUE PER LENGTH CLASS, then create the new unit, i.e.: convert from kg of fish caught by trawling for 1 h a standard bottom swept area of 0.45km2 (using a TVL trawl with 75 m sweeps at the standard speed of three knots) to... kg of fish per km^2 by dividing with 0.45
p1 <- ggplot(hlcodL3) +
geom_histogram(aes(length_cm2, fill = "length_cm1"), alpha = 0.5)
p2 <- ggplot(hlcodL3) +
geom_histogram(aes(length_cm2, fill = "length_cm2"), alpha = 0.5)
p1/p2
hlcodhaul <- hlcodL3 %>%
mutate(cpue_kg = CPUEst_kg,
cpue = CPUEst,
cpue_kg_un = CPUEun_kg,
cpue_un = CPUEun,
kg = cpue_kg * (HaulDur / 60),
abund = cpue * (HaulDur / 60),
density = kg/0.45,
density_ab = abund/0.45)
# t <- hlcodhaul %>% filter(haul_cpue_un == 0)
# t <- hlcodhaul %>% filter(!Country == "SWE") %>% filter(haul_cpue_un > 0)
hlflehaul <- hlfleL3 %>%
mutate(cpue_kg = CPUEst_kg,
cpue = CPUEst,
cpue_kg_un = CPUEun_kg,
cpue_un = CPUEun,
kg = cpue_kg * (HaulDur / 60),
abund = cpue * (HaulDur / 60),
density = kg/0.45,
density_ab = abund/0.45)
# First, figure out why i have length = 0 and density = 0 when I have other lengths in the haul
hlcodhaul %>% filter(haul.id == "1993:1:GFR:SOL:H20:23:31") %>% as.data.frame()
#> RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType StNo
#> 1 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 2 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 3 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 4 HH BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier NoMeas
#> 1 31 1993 W 126436 1 <NA> 6 1 3
#> 2 31 1993 W 126436 1 <NA> 6 1 3
#> 3 31 1993 W 126436 1 <NA> 6 1 3
#> 4 31 1993 <NA> NA zeroCatch <NA> NA NA NA
#> SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 1 NA 25 1 6 2 <NA>
#> 2 1 NA 25 1 35 2 <NA>
#> 3 1 NA 25 1 39 2 <NA>
#> 4 NA NA NA <NA> NA NA <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20211203 126436 SOL SOL
#> 2 NA 20211203 126436 SOL SOL
#> 3 NA 20211203 126436 SOL SOL
#> 4 NA 20220301 NA SOL SOL
#> IDx sub_div Rect HaulVal StdSpecRecCode BySpecRecCode
#> 1 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 2 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 3 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 4 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> Fishing.line Month DataType HaulDur GroundSpeed haul.id
#> 1 36 2 C 30 4 1993:1:GFR:SOL:H20:23:31
#> 2 36 2 C 30 4 1993:1:GFR:SOL:H20:23:31
#> 3 36 2 C 30 4 1993:1:GFR:SOL:H20:23:31
#> 4 36 2 C 30 4 1993:1:GFR:SOL:H20:23:31
#> ShootLat ShootLong id_haul_stomach Species CPUEun a
#> 1 54.5167 14.15 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 2 54.5167 14.15 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 3 54.5167 14.15 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 4 54.5167 14.15 <NA> Gadus morhua 0 0.007244481
#> b length_cm length_cm2 weight_kg CPUEun_kg RSA_sweep RS_sweep RS_x
#> 1 3.1008 6 6 0.001874558 0.003749116 1.47 0.75 0.75
#> 2 3.1008 35 35 0.444481726 0.888963452 1.47 0.75 0.75
#> 3 3.1008 39 39 0.621699998 1.243399996 1.47 0.75 0.75
#> 4 3.1008 NA 0 0.000000000 0.000000000 1.47 0.75 0.75
#> Horizontal.opening..m. Swep.one.side..after.formula...meter Size.final..m
#> 1 24.12 NA NA
#> 2 24.12 NA NA
#> 3 24.12 NA NA
#> 4 24.12 NA NA
#> Swept.area RSA_x RS RSA RSRSA CPUEst_kg CPUEst cpue_kg cpue
#> 1 NA -0.3333333 0.75 1.47 1.1025 0.0041334 2.205 0.0041334 2.205
#> 2 NA -0.3333333 0.75 1.47 1.1025 0.9800822 2.205 0.9800822 2.205
#> 3 NA -0.3333333 0.75 1.47 1.1025 1.3708485 2.205 1.3708485 2.205
#> 4 NA -0.3333333 0.75 1.47 1.1025 0.0000000 0.000 0.0000000 0.000
#> cpue_kg_un cpue_un kg abund density density_ab
#> 1 0.003749116 2 0.0020667 1.1025 0.004592667 2.45
#> 2 0.888963452 2 0.4900411 1.1025 1.088980229 2.45
#> 3 1.243399996 2 0.6854242 1.1025 1.523164995 2.45
#> 4 0.000000000 0 0.0000000 0.0000 0.000000000 0.00
hlcodhaul %>%
group_by(haul.id) %>%
mutate(no_catches = length(unique(CPUEun))) %>%
filter(any(CPUEun == 0)) %>%
filter(no_catches > 1) %>%
as.data.frame() %>%
head(50)
#> RecordType Survey Quarter Country Ship Gear SweepLngt GearEx DoorType StNo
#> 1 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 2 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 3 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 4 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 5 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 6 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 7 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 8 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 9 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 10 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 11 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 12 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 13 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 14 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 15 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 16 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 17 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 18 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 19 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 20 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 21 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 22 HL BITS 1 GFR 06S1 H20 NA <NA> NA 21
#> 23 HL BITS 1 GFR 06S1 H20 NA <NA> NA 22
#> 24 HL BITS 1 GFR 06S1 H20 NA <NA> NA 22
#> 25 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 26 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 27 HL BITS 1 GFR 06S1 H20 NA <NA> NA 23
#> 28 HL BITS 1 GFR 06S1 H20 NA <NA> NA 24
#> 29 HL BITS 1 GFR 06S1 H20 NA <NA> NA 24
#> 30 HL BITS 1 GFR 06S1 H20 NA <NA> NA 24
#> 31 HL BITS 1 GFR 06S1 H20 NA <NA> NA 24
#> 32 HL BITS 1 GFR 06S1 H20 NA <NA> NA 24
#> 33 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 34 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 35 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 36 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 37 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 38 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 39 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 40 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 41 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 42 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 43 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 44 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 45 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 46 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 47 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 48 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 49 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> 50 HL BITS 1 GFR 06S1 H20 NA <NA> NA 25
#> HaulNo Year SpecCodeType SpecCode SpecVal Sex TotalNo CatIdentifier NoMeas
#> 1 1 1993 W 126436 1 <NA> 110 1 55
#> 2 1 1993 W 126436 1 <NA> 110 1 55
#> 3 1 1993 W 126436 1 <NA> 110 1 55
#> 4 1 1993 W 126436 1 <NA> 110 1 55
#> 5 1 1993 W 126436 1 <NA> 110 1 55
#> 6 1 1993 W 126436 1 <NA> 110 1 55
#> 7 1 1993 W 126436 1 <NA> 110 1 55
#> 8 1 1993 W 126436 1 <NA> 110 1 55
#> 9 1 1993 W 126436 1 <NA> 110 1 55
#> 10 1 1993 W 126436 1 <NA> 110 1 55
#> 11 1 1993 W 126436 1 <NA> 110 1 55
#> 12 1 1993 W 126436 1 <NA> 110 1 55
#> 13 1 1993 W 126436 1 <NA> 110 1 55
#> 14 1 1993 W 126436 1 <NA> 110 1 55
#> 15 1 1993 W 126436 1 <NA> 110 1 55
#> 16 1 1993 W 126436 1 <NA> 110 1 55
#> 17 1 1993 W 126436 1 <NA> 110 1 55
#> 18 1 1993 W 126436 1 <NA> 110 1 55
#> 19 1 1993 W 126436 1 <NA> 110 1 55
#> 20 1 1993 W 126436 1 <NA> 110 1 55
#> 21 1 1993 W 126436 1 <NA> 110 1 55
#> 22 1 1993 W 126436 1 <NA> 110 1 55
#> 23 32 1993 W 126436 1 <NA> 4 1 2
#> 24 32 1993 W 126436 1 <NA> 4 1 2
#> 25 31 1993 W 126436 1 <NA> 6 1 3
#> 26 31 1993 W 126436 1 <NA> 6 1 3
#> 27 31 1993 W 126436 1 <NA> 6 1 3
#> 28 30 1993 W 126436 1 <NA> 10 1 5
#> 29 30 1993 W 126436 1 <NA> 10 1 5
#> 30 30 1993 W 126436 1 <NA> 10 1 5
#> 31 30 1993 W 126436 1 <NA> 10 1 5
#> 32 30 1993 W 126436 1 <NA> 10 1 5
#> 33 2 1993 W 126436 1 <NA> 998 1 153
#> 34 2 1993 W 126436 1 <NA> 998 1 153
#> 35 2 1993 W 126436 1 <NA> 998 1 153
#> 36 2 1993 W 126436 1 <NA> 998 1 153
#> 37 2 1993 W 126436 1 <NA> 998 1 153
#> 38 2 1993 W 126436 1 <NA> 998 1 153
#> 39 2 1993 W 126436 1 <NA> 998 1 153
#> 40 2 1993 W 126436 1 <NA> 998 1 153
#> 41 2 1993 W 126436 1 <NA> 998 1 153
#> 42 2 1993 W 126436 1 <NA> 998 1 153
#> 43 2 1993 W 126436 1 <NA> 998 1 153
#> 44 2 1993 W 126436 1 <NA> 998 1 153
#> 45 2 1993 W 126436 1 <NA> 998 1 153
#> 46 2 1993 W 126436 1 <NA> 998 1 153
#> 47 2 1993 W 126436 1 <NA> 998 1 153
#> 48 2 1993 W 126436 1 <NA> 998 1 153
#> 49 2 1993 W 126436 1 <NA> 998 1 153
#> 50 2 1993 W 126436 1 <NA> 998 1 153
#> SubFactor SubWgt CatCatchWgt LngtCode LngtClass HLNoAtLngt DevStage
#> 1 1 NA 680 1 14 4 <NA>
#> 2 1 NA 680 1 16 4 <NA>
#> 3 1 NA 680 1 18 4 <NA>
#> 4 1 NA 680 1 27 8 <NA>
#> 5 1 NA 680 1 28 8 <NA>
#> 6 1 NA 680 1 29 8 <NA>
#> 7 1 NA 680 1 58 2 <NA>
#> 8 1 NA 680 1 59 2 <NA>
#> 9 1 NA 680 1 60 2 <NA>
#> 10 1 NA 680 1 65 2 <NA>
#> 11 1 NA 680 1 37 2 <NA>
#> 12 1 NA 680 1 44 4 <NA>
#> 13 1 NA 680 1 48 2 <NA>
#> 14 1 NA 680 1 49 2 <NA>
#> 15 1 NA 680 1 50 4 <NA>
#> 16 1 NA 680 1 53 2 <NA>
#> 17 1 NA 680 1 30 8 <NA>
#> 18 1 NA 680 1 31 10 <NA>
#> 19 1 NA 680 1 32 8 <NA>
#> 20 1 NA 680 1 33 14 <NA>
#> 21 1 NA 680 1 34 6 <NA>
#> 22 1 NA 680 1 35 4 <NA>
#> 23 1 NA 41 1 42 2 <NA>
#> 24 1 NA 41 1 47 2 <NA>
#> 25 1 NA 25 1 6 2 <NA>
#> 26 1 NA 25 1 35 2 <NA>
#> 27 1 NA 25 1 39 2 <NA>
#> 28 1 NA 78 1 33 2 <NA>
#> 29 1 NA 78 1 35 2 <NA>
#> 30 1 NA 78 1 37 2 <NA>
#> 31 1 NA 78 1 44 2 <NA>
#> 32 1 NA 78 1 52 2 <NA>
#> 33 1 NA 3260 1 20 7 <NA>
#> 34 1 NA 3260 1 21 13 <NA>
#> 35 1 NA 3260 1 24 13 <NA>
#> 36 1 NA 3260 1 25 46 <NA>
#> 37 1 NA 3260 1 26 46 <NA>
#> 38 1 NA 3260 1 27 130 <NA>
#> 39 1 NA 3260 1 41 7 <NA>
#> 40 1 NA 3260 1 46 13 <NA>
#> 41 1 NA 3260 1 47 7 <NA>
#> 42 1 NA 3260 1 49 7 <NA>
#> 43 1 NA 3260 1 34 52 <NA>
#> 44 1 NA 3260 1 35 26 <NA>
#> 45 1 NA 3260 1 36 20 <NA>
#> 46 1 NA 3260 1 37 7 <NA>
#> 47 1 NA 3260 1 38 13 <NA>
#> 48 1 NA 3260 1 39 7 <NA>
#> 49 1 NA 3260 1 28 130 <NA>
#> 50 1 NA 3260 1 29 117 <NA>
#> LenMeasType DateofCalculation Valid_Aphia Ship2 Ship3
#> 1 NA 20211203 126436 SOL SOL
#> 2 NA 20211203 126436 SOL SOL
#> 3 NA 20211203 126436 SOL SOL
#> 4 NA 20211203 126436 SOL SOL
#> 5 NA 20211203 126436 SOL SOL
#> 6 NA 20211203 126436 SOL SOL
#> 7 NA 20211203 126436 SOL SOL
#> 8 NA 20211203 126436 SOL SOL
#> 9 NA 20211203 126436 SOL SOL
#> 10 NA 20211203 126436 SOL SOL
#> 11 NA 20211203 126436 SOL SOL
#> 12 NA 20211203 126436 SOL SOL
#> 13 NA 20211203 126436 SOL SOL
#> 14 NA 20211203 126436 SOL SOL
#> 15 NA 20211203 126436 SOL SOL
#> 16 NA 20211203 126436 SOL SOL
#> 17 NA 20211203 126436 SOL SOL
#> 18 NA 20211203 126436 SOL SOL
#> 19 NA 20211203 126436 SOL SOL
#> 20 NA 20211203 126436 SOL SOL
#> 21 NA 20211203 126436 SOL SOL
#> 22 NA 20211203 126436 SOL SOL
#> 23 NA 20211203 126436 SOL SOL
#> 24 NA 20211203 126436 SOL SOL
#> 25 NA 20211203 126436 SOL SOL
#> 26 NA 20211203 126436 SOL SOL
#> 27 NA 20211203 126436 SOL SOL
#> 28 NA 20211203 126436 SOL SOL
#> 29 NA 20211203 126436 SOL SOL
#> 30 NA 20211203 126436 SOL SOL
#> 31 NA 20211203 126436 SOL SOL
#> 32 NA 20211203 126436 SOL SOL
#> 33 NA 20211203 126436 SOL SOL
#> 34 NA 20211203 126436 SOL SOL
#> 35 NA 20211203 126436 SOL SOL
#> 36 NA 20211203 126436 SOL SOL
#> 37 NA 20211203 126436 SOL SOL
#> 38 NA 20211203 126436 SOL SOL
#> 39 NA 20211203 126436 SOL SOL
#> 40 NA 20211203 126436 SOL SOL
#> 41 NA 20211203 126436 SOL SOL
#> 42 NA 20211203 126436 SOL SOL
#> 43 NA 20211203 126436 SOL SOL
#> 44 NA 20211203 126436 SOL SOL
#> 45 NA 20211203 126436 SOL SOL
#> 46 NA 20211203 126436 SOL SOL
#> 47 NA 20211203 126436 SOL SOL
#> 48 NA 20211203 126436 SOL SOL
#> 49 NA 20211203 126436 SOL SOL
#> 50 NA 20211203 126436 SOL SOL
#> IDx sub_div Rect HaulVal StdSpecRecCode BySpecRecCode
#> 1 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 2 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 3 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 4 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 5 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 6 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 7 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 8 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 9 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 10 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 11 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 12 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 13 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 14 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 15 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 16 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 17 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 18 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 19 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 20 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 21 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 22 1993.1.GFR.06S1.H20.21.1 24 38G3 V 1 1
#> 23 1993.1.GFR.06S1.H20.22.32 24 38G4 V 1 1
#> 24 1993.1.GFR.06S1.H20.22.32 24 38G4 V 1 1
#> 25 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 26 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 27 1993.1.GFR.06S1.H20.23.31 24 38G4 V 1 1
#> 28 1993.1.GFR.06S1.H20.24.30 24 37G3 V 1 1
#> 29 1993.1.GFR.06S1.H20.24.30 24 37G3 V 1 1
#> 30 1993.1.GFR.06S1.H20.24.30 24 37G3 V 1 1
#> 31 1993.1.GFR.06S1.H20.24.30 24 37G3 V 1 1
#> 32 1993.1.GFR.06S1.H20.24.30 24 37G3 V 1 1
#> 33 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 34 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 35 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 36 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 37 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 38 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 39 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 40 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 41 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 42 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 43 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 44 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 45 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 46 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 47 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 48 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 49 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> 50 1993.1.GFR.06S1.H20.25.2 24 38G3 V 1 1
#> Fishing.line Month DataType HaulDur GroundSpeed haul.id
#> 1 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 2 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 3 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 4 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 5 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 6 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 7 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 8 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 9 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 10 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 11 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 12 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 13 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 14 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 15 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 16 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 17 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 18 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 19 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 20 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 21 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 22 36 2 C 30 3.8 1993:1:GFR:SOL:H20:21:1
#> 23 36 2 C 30 3.6 1993:1:GFR:SOL:H20:22:32
#> 24 36 2 C 30 3.6 1993:1:GFR:SOL:H20:22:32
#> 25 36 2 C 30 4.0 1993:1:GFR:SOL:H20:23:31
#> 26 36 2 C 30 4.0 1993:1:GFR:SOL:H20:23:31
#> 27 36 2 C 30 4.0 1993:1:GFR:SOL:H20:23:31
#> 28 36 2 C 30 3.8 1993:1:GFR:SOL:H20:24:30
#> 29 36 2 C 30 3.8 1993:1:GFR:SOL:H20:24:30
#> 30 36 2 C 30 3.8 1993:1:GFR:SOL:H20:24:30
#> 31 36 2 C 30 3.8 1993:1:GFR:SOL:H20:24:30
#> 32 36 2 C 30 3.8 1993:1:GFR:SOL:H20:24:30
#> 33 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 34 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 35 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 36 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 37 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 38 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 39 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 40 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 41 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 42 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 43 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 44 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 45 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 46 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 47 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 48 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 49 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> 50 36 2 C 30 3.6 1993:1:GFR:SOL:H20:25:2
#> ShootLat ShootLong id_haul_stomach Species CPUEun a
#> 1 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 2 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 3 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 4 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 8 0.007244481
#> 5 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 8 0.007244481
#> 6 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 8 0.007244481
#> 7 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 8 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 9 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 10 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 11 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 12 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 13 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 14 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 15 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 16 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 2 0.007244481
#> 17 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 8 0.007244481
#> 18 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 10 0.007244481
#> 19 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 8 0.007244481
#> 20 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 14 0.007244481
#> 21 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 6 0.007244481
#> 22 54.6833 13.0000 1993.1.2.GFR.38G3.1 Gadus morhua 4 0.007244481
#> 23 54.5167 14.2667 1993.1.2.GFR.38G4.32 Gadus morhua 2 0.007244481
#> 24 54.5167 14.2667 1993.1.2.GFR.38G4.32 Gadus morhua 2 0.007244481
#> 25 54.5167 14.1500 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 26 54.5167 14.1500 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 27 54.5167 14.1500 1993.1.2.GFR.38G4.31 Gadus morhua 2 0.007244481
#> 28 54.4833 13.9833 1993.1.2.GFR.37G3.30 Gadus morhua 2 0.007244481
#> 29 54.4833 13.9833 1993.1.2.GFR.37G3.30 Gadus morhua 2 0.007244481
#> 30 54.4833 13.9833 1993.1.2.GFR.37G3.30 Gadus morhua 2 0.007244481
#> 31 54.4833 13.9833 1993.1.2.GFR.37G3.30 Gadus morhua 2 0.007244481
#> 32 54.4833 13.9833 1993.1.2.GFR.37G3.30 Gadus morhua 2 0.007244481
#> 33 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 34 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 13 0.007244481
#> 35 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 13 0.007244481
#> 36 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 46 0.007244481
#> 37 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 46 0.007244481
#> 38 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 130 0.007244481
#> 39 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 40 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 13 0.007244481
#> 41 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 42 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 43 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 52 0.007244481
#> 44 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 26 0.007244481
#> 45 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 20 0.007244481
#> 46 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 47 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 13 0.007244481
#> 48 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 7 0.007244481
#> 49 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 130 0.007244481
#> 50 54.7333 13.0833 1993.1.2.GFR.38G3.2 Gadus morhua 117 0.007244481
#> b length_cm length_cm2 weight_kg CPUEun_kg RSA_sweep RS_sweep
#> 1 3.1008 14 14 0.025937100 0.103748400 1.47 0.79
#> 2 3.1008 16 16 0.039241253 0.156965010 1.47 0.79
#> 3 3.1008 18 18 0.056540106 0.226160424 1.47 0.79
#> 4 3.1008 27 27 0.198783552 1.590268412 1.47 0.79
#> 5 3.1008 28 28 0.222512961 1.780103692 1.47 0.79
#> 6 3.1008 29 29 0.248091227 1.984729814 1.47 0.79
#> 7 3.1008 58 58 2.128361064 4.256722129 1.47 0.79
#> 8 3.1008 59 59 2.244221418 4.488442837 1.47 0.79
#> 9 3.1008 60 60 2.364281503 4.728563006 1.47 0.79
#> 10 3.1008 65 65 3.030327268 6.060654536 1.47 0.79
#> 11 3.1008 37 37 0.528065302 1.056130604 1.47 0.79
#> 12 3.1008 44 44 0.903703173 3.614812692 1.47 0.79
#> 13 3.1008 48 48 1.183588094 2.367176188 1.47 0.79
#> 14 3.1008 49 49 1.261733885 2.523467769 1.47 0.79
#> 15 3.1008 50 50 1.343302904 5.373211615 1.47 0.79
#> 16 3.1008 53 53 1.609319924 3.218639848 1.47 0.79
#> 17 3.1008 30 30 0.275591162 2.204729295 1.47 0.79
#> 18 3.1008 31 31 0.305085835 3.050858346 1.47 0.79
#> 19 3.1008 32 32 0.336648559 2.693188473 1.47 0.79
#> 20 3.1008 33 33 0.370352888 5.184940437 1.47 0.79
#> 21 3.1008 34 34 0.406272607 2.437635642 1.47 0.79
#> 22 3.1008 35 35 0.444481726 1.777926905 1.47 0.79
#> 23 3.1008 42 42 0.782310468 1.564620936 1.47 0.83
#> 24 3.1008 47 47 1.108788707 2.217577415 1.47 0.83
#> 25 3.1008 6 6 0.001874558 0.003749116 1.47 0.75
#> 26 3.1008 35 35 0.444481726 0.888963452 1.47 0.75
#> 27 3.1008 39 39 0.621699998 1.243399996 1.47 0.75
#> 28 3.1008 33 33 0.370352888 0.740705777 1.47 0.79
#> 29 3.1008 35 35 0.444481726 0.888963452 1.47 0.79
#> 30 3.1008 37 37 0.528065302 1.056130604 1.47 0.79
#> 31 3.1008 44 44 0.903703173 1.807406346 1.47 0.79
#> 32 3.1008 52 52 1.517018710 3.034037419 1.47 0.79
#> 33 3.1008 20 20 0.078386533 0.548705733 1.47 0.83
#> 34 3.1008 21 21 0.091189586 1.185464615 1.47 0.83
#> 35 3.1008 24 24 0.137964290 1.793535765 1.47 0.83
#> 36 3.1008 25 25 0.156581358 7.202742459 1.47 0.83
#> 37 3.1008 26 26 0.176830444 8.134200440 1.47 0.83
#> 38 3.1008 27 27 0.198783552 25.841861699 1.47 0.83
#> 39 3.1008 41 41 0.725985458 5.081898208 1.47 0.83
#> 40 3.1008 46 46 1.037259063 13.484367814 1.47 0.83
#> 41 3.1008 47 47 1.108788707 7.761520951 1.47 0.83
#> 42 3.1008 49 49 1.261733885 8.832137193 1.47 0.83
#> 43 3.1008 34 34 0.406272607 21.126175567 1.47 0.83
#> 44 3.1008 35 35 0.444481726 11.556524879 1.47 0.83
#> 45 3.1008 36 36 0.485054476 9.701089523 1.47 0.83
#> 46 3.1008 37 37 0.528065302 3.696457113 1.47 0.83
#> 47 3.1008 38 38 0.573588857 7.456655137 1.47 0.83
#> 48 3.1008 39 39 0.621699998 4.351899986 1.47 0.83
#> 49 3.1008 28 28 0.222512961 28.926684992 1.47 0.83
#> 50 3.1008 29 29 0.248091227 29.026673534 1.47 0.83
#> RS_x Horizontal.opening..m. Swep.one.side..after.formula...meter
#> 1 0.7894737 24.12 NA
#> 2 0.7894737 24.12 NA
#> 3 0.7894737 24.12 NA
#> 4 0.7894737 24.12 NA
#> 5 0.7894737 24.12 NA
#> 6 0.7894737 24.12 NA
#> 7 0.7894737 24.12 NA
#> 8 0.7894737 24.12 NA
#> 9 0.7894737 24.12 NA
#> 10 0.7894737 24.12 NA
#> 11 0.7894737 24.12 NA
#> 12 0.7894737 24.12 NA
#> 13 0.7894737 24.12 NA
#> 14 0.7894737 24.12 NA
#> 15 0.7894737 24.12 NA
#> 16 0.7894737 24.12 NA
#> 17 0.7894737 24.12 NA
#> 18 0.7894737 24.12 NA
#> 19 0.7894737 24.12 NA
#> 20 0.7894737 24.12 NA
#> 21 0.7894737 24.12 NA
#> 22 0.7894737 24.12 NA
#> 23 0.8333333 24.12 NA
#> 24 0.8333333 24.12 NA
#> 25 0.7500000 24.12 NA
#> 26 0.7500000 24.12 NA
#> 27 0.7500000 24.12 NA
#> 28 0.7894737 24.12 NA
#> 29 0.7894737 24.12 NA
#> 30 0.7894737 24.12 NA
#> 31 0.7894737 24.12 NA
#> 32 0.7894737 24.12 NA
#> 33 0.8333333 24.12 NA
#> 34 0.8333333 24.12 NA
#> 35 0.8333333 24.12 NA
#> 36 0.8333333 24.12 NA
#> 37 0.8333333 24.12 NA
#> 38 0.8333333 24.12 NA
#> 39 0.8333333 24.12 NA
#> 40 0.8333333 24.12 NA
#> 41 0.8333333 24.12 NA
#> 42 0.8333333 24.12 NA
#> 43 0.8333333 24.12 NA
#> 44 0.8333333 24.12 NA
#> 45 0.8333333 24.12 NA
#> 46 0.8333333 24.12 NA
#> 47 0.8333333 24.12 NA
#> 48 0.8333333 24.12 NA
#> 49 0.8333333 24.12 NA
#> 50 0.8333333 24.12 NA
#> Size.final..m Swept.area RSA_x RS RSA RSRSA CPUEst_kg CPUEst
#> 1 NA NA -0.3333333 0.79 1.47 1.1613 0.1204830 4.6452
#> 2 NA NA -0.3333333 0.79 1.47 1.1613 0.1822835 4.6452
#> 3 NA NA -0.3333333 0.79 1.47 1.1613 0.2626401 4.6452
#> 4 NA NA -0.3333333 0.79 1.47 1.1613 1.8467787 9.2904
#> 5 NA NA -0.3333333 0.79 1.47 1.1613 2.0672344 9.2904
#> 6 NA NA -0.3333333 0.79 1.47 1.1613 2.3048667 9.2904
#> 7 NA NA -0.3333333 0.79 1.47 1.1613 4.9433314 2.3226
#> 8 NA NA -0.3333333 0.79 1.47 1.1613 5.2124287 2.3226
#> 9 NA NA -0.3333333 0.79 1.47 1.1613 5.4912802 2.3226
#> 10 NA NA -0.3333333 0.79 1.47 1.1613 7.0382381 2.3226
#> 11 NA NA -0.3333333 0.79 1.47 1.1613 1.2264845 2.3226
#> 12 NA NA -0.3333333 0.79 1.47 1.1613 4.1978820 4.6452
#> 13 NA NA -0.3333333 0.79 1.47 1.1613 2.7490017 2.3226
#> 14 NA NA -0.3333333 0.79 1.47 1.1613 2.9305031 2.3226
#> 15 NA NA -0.3333333 0.79 1.47 1.1613 6.2399106 4.6452
#> 16 NA NA -0.3333333 0.79 1.47 1.1613 3.7378065 2.3226
#> 17 NA NA -0.3333333 0.79 1.47 1.1613 2.5603521 9.2904
#> 18 NA NA -0.3333333 0.79 1.47 1.1613 3.5429618 11.6130
#> 19 NA NA -0.3333333 0.79 1.47 1.1613 3.1275998 9.2904
#> 20 NA NA -0.3333333 0.79 1.47 1.1613 6.0212713 16.2582
#> 21 NA NA -0.3333333 0.79 1.47 1.1613 2.8308263 6.9678
#> 22 NA NA -0.3333333 0.79 1.47 1.1613 2.0647065 4.6452
#> 23 NA NA -0.3333333 0.83 1.47 1.2201 1.9089940 2.4402
#> 24 NA NA -0.3333333 0.83 1.47 1.2201 2.7056662 2.4402
#> 25 NA NA -0.3333333 0.75 1.47 1.1025 0.0041334 2.2050
#> 26 NA NA -0.3333333 0.75 1.47 1.1025 0.9800822 2.2050
#> 27 NA NA -0.3333333 0.75 1.47 1.1025 1.3708485 2.2050
#> 28 NA NA -0.3333333 0.79 1.47 1.1613 0.8601816 2.3226
#> 29 NA NA -0.3333333 0.79 1.47 1.1613 1.0323533 2.3226
#> 30 NA NA -0.3333333 0.79 1.47 1.1613 1.2264845 2.3226
#> 31 NA NA -0.3333333 0.79 1.47 1.1613 2.0989410 2.3226
#> 32 NA NA -0.3333333 0.79 1.47 1.1613 3.5234277 2.3226
#> 33 NA NA -0.3333333 0.83 1.47 1.2201 0.6694759 8.5407
#> 34 NA NA -0.3333333 0.83 1.47 1.2201 1.4463854 15.8613
#> 35 NA NA -0.3333333 0.83 1.47 1.2201 2.1882930 15.8613
#> 36 NA NA -0.3333333 0.83 1.47 1.2201 8.7880661 56.1246
#> 37 NA NA -0.3333333 0.83 1.47 1.2201 9.9245380 56.1246
#> 38 NA NA -0.3333333 0.83 1.47 1.2201 31.5296555 158.6130
#> 39 NA NA -0.3333333 0.83 1.47 1.2201 6.2004240 8.5407
#> 40 NA NA -0.3333333 0.83 1.47 1.2201 16.4522772 15.8613
#> 41 NA NA -0.3333333 0.83 1.47 1.2201 9.4698317 8.5407
#> 42 NA NA -0.3333333 0.83 1.47 1.2201 10.7760906 8.5407
#> 43 NA NA -0.3333333 0.83 1.47 1.2201 25.7760468 63.4452
#> 44 NA NA -0.3333333 0.83 1.47 1.2201 14.1001160 31.7226
#> 45 NA NA -0.3333333 0.83 1.47 1.2201 11.8362993 24.4020
#> 46 NA NA -0.3333333 0.83 1.47 1.2201 4.5100473 8.5407
#> 47 NA NA -0.3333333 0.83 1.47 1.2201 9.0978649 15.8613
#> 48 NA NA -0.3333333 0.83 1.47 1.2201 5.3097532 8.5407
#> 49 NA NA -0.3333333 0.83 1.47 1.2201 35.2934484 158.6130
#> 50 NA NA -0.3333333 0.83 1.47 1.2201 35.4154444 142.7517
#> cpue_kg cpue cpue_kg_un cpue_un kg abund density
#> 1 0.1204830 4.6452 0.103748400 4 0.06024151 2.32260 0.133870018
#> 2 0.1822835 4.6452 0.156965010 4 0.09114173 2.32260 0.202537185
#> 3 0.2626401 4.6452 0.226160424 4 0.13132005 2.32260 0.291822333
#> 4 1.8467787 9.2904 1.590268412 8 0.92338935 4.64520 2.051976341
#> 5 2.0672344 9.2904 1.780103692 8 1.03361721 4.64520 2.296927130
#> 6 2.3048667 9.2904 1.984729814 8 1.15243337 4.64520 2.560963037
#> 7 4.9433314 2.3226 4.256722129 2 2.47166570 1.16130 5.492590453
#> 8 5.2124287 2.3226 4.488442837 2 2.60621433 1.16130 5.791587407
#> 9 5.4912802 2.3226 4.728563006 2 2.74564011 1.16130 6.101422465
#> 10 7.0382381 2.3226 6.060654536 2 3.51911906 1.16130 7.820264570
#> 11 1.2264845 2.3226 1.056130604 2 0.61324224 1.16130 1.362760522
#> 12 4.1978820 4.6452 3.614812692 4 2.09894099 2.32260 4.664313310
#> 13 2.7490017 2.3226 2.367176188 2 1.37450085 1.16130 3.054446341
#> 14 2.9305031 2.3226 2.523467769 2 1.46525156 1.16130 3.256114579
#> 15 6.2399106 4.6452 5.373211615 4 3.11995532 2.32260 6.933234054
#> 16 3.7378065 2.3226 3.218639848 2 1.86890323 1.16130 4.153118283
#> 17 2.5603521 9.2904 2.204729295 8 1.28017607 4.64520 2.844835700
#> 18 3.5429618 11.6130 3.050858346 10 1.77148090 5.80650 3.936624220
#> 19 3.1275998 9.2904 2.693188473 8 1.56379989 4.64520 3.475110860
#> 20 6.0212713 16.2582 5.184940437 14 3.01063566 8.12910 6.690301477
#> 21 2.8308263 6.9678 2.437635642 6 1.41541314 3.48390 3.145362524
#> 22 2.0647065 4.6452 1.777926905 4 1.03235326 2.32260 2.294118349
#> 23 1.9089940 2.4402 1.564620936 2 0.95449700 1.22010 2.121104449
#> 24 2.7056662 2.4402 2.217577415 2 1.35283310 1.22010 3.006295782
#> 25 0.0041334 2.2050 0.003749116 2 0.00206670 1.10250 0.004592667
#> 26 0.9800822 2.2050 0.888963452 2 0.49004110 1.10250 1.088980229
#> 27 1.3708485 2.2050 1.243399996 2 0.68542425 1.10250 1.523164995
#> 28 0.8601816 2.3226 0.740705777 2 0.43009081 1.16130 0.955757354
#> 29 1.0323533 2.3226 0.888963452 2 0.51617663 1.16130 1.147059175
#> 30 1.2264845 2.3226 1.056130604 2 0.61324224 1.16130 1.362760522
#> 31 2.0989410 2.3226 1.807406346 2 1.04947049 1.16130 2.332156655
#> 32 3.5234277 2.3226 3.034037419 2 1.76171383 1.16130 3.914919617
#> 33 0.6694759 8.5407 0.548705733 7 0.33473793 4.27035 0.743862072
#> 34 1.4463854 15.8613 1.185464615 13 0.72319269 7.93065 1.607094863
#> 35 2.1882930 15.8613 1.793535765 13 1.09414649 7.93065 2.431436652
#> 36 8.7880661 56.1246 7.202742459 46 4.39403304 28.06230 9.764517861
#> 37 9.9245380 56.1246 8.134200440 46 4.96226898 28.06230 11.027264397
#> 38 31.5296555 158.6130 25.841861699 130 15.76482773 79.30650 35.032950510
#> 39 6.2004240 8.5407 5.081898208 7 3.10021200 4.27035 6.889360005
#> 40 16.4522772 15.8613 13.484367814 13 8.22613858 7.93065 18.280307966
#> 41 9.4698317 8.5407 7.761520951 7 4.73491586 4.27035 10.522035236
#> 42 10.7760906 8.5407 8.832137193 7 5.38804529 4.27035 11.973433988
#> 43 25.7760468 63.4452 21.126175567 52 12.88802340 31.72260 28.640052010
#> 44 14.1001160 31.7226 11.556524879 26 7.05005800 15.86130 15.666795562
#> 45 11.8362993 24.4020 9.701089523 20 5.91814966 12.20100 13.151443697
#> 46 4.5100473 8.5407 3.696457113 7 2.25502366 4.27035 5.011163693
#> 47 9.0978649 15.8613 7.456655137 13 4.54893247 7.93065 10.108738814
#> 48 5.3097532 8.5407 4.351899986 7 2.65487659 4.27035 5.899725747
#> 49 35.2934484 158.6130 28.926684992 130 17.64672418 79.30650 39.214942620
#> 50 35.4154444 142.7517 29.026673534 117 17.70772219 71.37585 39.350493755
#> density_ab no_catches
#> 1 5.161333 7
#> 2 5.161333 7
#> 3 5.161333 7
#> 4 10.322667 7
#> 5 10.322667 7
#> 6 10.322667 7
#> 7 2.580667 7
#> 8 2.580667 7
#> 9 2.580667 7
#> 10 2.580667 7
#> 11 2.580667 7
#> 12 5.161333 7
#> 13 2.580667 7
#> 14 2.580667 7
#> 15 5.161333 7
#> 16 2.580667 7
#> 17 10.322667 7
#> 18 12.903333 7
#> 19 10.322667 7
#> 20 18.064667 7
#> 21 7.742000 7
#> 22 5.161333 7
#> 23 2.711333 2
#> 24 2.711333 2
#> 25 2.450000 2
#> 26 2.450000 2
#> 27 2.450000 2
#> 28 2.580667 2
#> 29 2.580667 2
#> 30 2.580667 2
#> 31 2.580667 2
#> 32 2.580667 2
#> 33 9.489667 12
#> 34 17.623667 12
#> 35 17.623667 12
#> 36 62.360667 12
#> 37 62.360667 12
#> 38 176.236667 12
#> 39 9.489667 12
#> 40 17.623667 12
#> 41 9.489667 12
#> 42 9.489667 12
#> 43 70.494667 12
#> 44 35.247333 12
#> 45 27.113333 12
#> 46 9.489667 12
#> 47 17.623667 12
#> 48 9.489667 12
#> 49 176.236667 12
#> 50 158.613000 12
hlcodhaul %>%
group_by(haul.id) %>%
filter(CPUEun == min(CPUEun)) %>%
ungroup() %>%
distinct(CPUEun)
#> # A tibble: 1 × 1
#> CPUEun
#> <dbl>
#> 1 0
# The minimum CPUE in all hauls is always zero at this stage. It doesn't really matter because I calculate haul-level CPUE by grouping by ID's and summing. But let's remove them anyway
hlcodhaul %>%
group_by(haul.id) %>%
summarise(cpue_haul = sum(cpue)) %>%
ungroup() %>%
filter(!cpue_haul == 0)
#> # A tibble: 8,239 × 2
#> haul.id cpue_haul
#> <chr> <dbl>
#> 1 1993:1:GFR:SOL:H20:21:1 128.
#> 2 1993:1:GFR:SOL:H20:22:32 4.88
#> 3 1993:1:GFR:SOL:H20:23:31 6.62
#> 4 1993:1:GFR:SOL:H20:24:30 11.6
#> 5 1993:1:GFR:SOL:H20:25:2 1220.
#> 6 1993:1:GFR:SOL:H20:26:3 478.
#> 7 1993:1:GFR:SOL:H20:27:27 31.7
#> 8 1993:1:GFR:SOL:H20:28:24 3548.
#> 9 1993:1:GFR:SOL:H20:29:29 22.0
#> 10 1993:1:GFR:SOL:H20:30:28 2.44
#> # … with 8,229 more rows
# Rename columns and select specific columns from the cod data
datcod <- hlcodhaul %>%
dplyr::select(density, Year, ShootLat, ShootLong, Quarter, Country, Month, haul.id, IDx, Rect, sub_div, length_cm2, id_haul_stomach) %>%
rename(year = Year,
lat = ShootLat,
lon = ShootLong,
quarter = Quarter,
ices_rect = Rect,
length_cm = length_cm2) %>%
mutate(species = "cod")
# Now do the same for flounder
# Rename columns and select specific columns from the cod data
datfle <- hlflehaul %>%
dplyr::select(density, Year, ShootLat, ShootLong, Quarter, Country, Month, haul.id, IDx, Rect, sub_div, length_cm2, id_haul_stomach) %>%
rename(year = Year,
lat = ShootLat,
lon = ShootLong,
quarter = Quarter,
ices_rect = Rect,
length_cm = length_cm2) %>%
mutate(species = "flounder")# Because it's size-based cpue, I want the data frame to be "full", so that each haul has every size, even if all are empty. Now I only have lengths with catches, and no lengths if catch is zero.
datcod %>% group_by(haul.id) %>% summarise(n_size = length(unique(length_cm))) %>% distinct(n_size, .keep_all = TRUE)
#> group_by: one grouping variable (haul.id)
#> summarise: now 9,373 rows and 2 columns, ungrouped
#> distinct: removed 9,304 rows (99%), 69 rows remaining
#> # A tibble: 69 × 2
#> haul.id n_size
#> <chr> <int>
#> 1 1993:1:GFR:SOL:H20:21:1 23
#> 2 1993:1:GFR:SOL:H20:22:32 3
#> 3 1993:1:GFR:SOL:H20:23:31 4
#> 4 1993:1:GFR:SOL:H20:24:30 6
#> 5 1993:1:GFR:SOL:H20:26:3 31
#> 6 1993:1:GFR:SOL:H20:27:27 10
#> 7 1993:1:GFR:SOL:H20:28:24 36
#> 8 1993:1:GFR:SOL:H20:29:29 7
#> 9 1993:1:GFR:SOL:H20:30:28 2
#> 10 1993:1:GFR:SOL:H20:31:25 29
#> # … with 59 more rows
datcod %>% filter(haul.id == "1993:1:GFR:SOL:H20:23:31") %>% as.data.frame()
#> filter: removed 219,786 rows (>99%), 4 rows remaining
#> density year lat lon quarter Country Month haul.id
#> 1 0.004592667 1993 54.5167 14.15 1 GFR 2 1993:1:GFR:SOL:H20:23:31
#> 2 1.088980229 1993 54.5167 14.15 1 GFR 2 1993:1:GFR:SOL:H20:23:31
#> 3 1.523164995 1993 54.5167 14.15 1 GFR 2 1993:1:GFR:SOL:H20:23:31
#> 4 0.000000000 1993 54.5167 14.15 1 GFR 2 1993:1:GFR:SOL:H20:23:31
#> IDx ices_rect sub_div length_cm id_haul_stomach
#> 1 1993.1.GFR.06S1.H20.23.31 38G4 24 6 1993.1.2.GFR.38G4.31
#> 2 1993.1.GFR.06S1.H20.23.31 38G4 24 35 1993.1.2.GFR.38G4.31
#> 3 1993.1.GFR.06S1.H20.23.31 38G4 24 39 1993.1.2.GFR.38G4.31
#> 4 1993.1.GFR.06S1.H20.23.31 38G4 24 0 <NA>
#> species
#> 1 cod
#> 2 cod
#> 3 cod
#> 4 cod
datcod %>% group_by(haul.id) %>% mutate(tot_dens = sum(density)) %>% ungroup() %>% distinct(haul.id, .keep_all = TRUE) %>% filter(tot_dens == 0)
#> group_by: one grouping variable (haul.id)
#> mutate (grouped): new variable 'tot_dens' (double) with 8,227 unique values and 0% NA
#> ungroup: no grouping variables
#> distinct: removed 210,417 rows (96%), 9,373 rows remaining
#> filter: removed 8,239 rows (88%), 1,134 rows remaining
#> # A tibble: 1,134 × 15
#> density year lat lon quarter Country Month haul.id IDx ices_…¹ sub_div
#> <dbl> <int> <dbl> <dbl> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 0 1993 55 17.5 1 GFR 2 1993:1… 1993… 39G7 25
#> 2 0 1993 57.9 19.4 1 SWE 3 1993:1… 1993… 44G9 28
#> 3 0 1993 57.1 17.9 3 SWE 8 1993:3… 1993… 43G7 27
#> 4 0 1993 57.1 18.9 3 SWE 8 1993:3… 1993… 43G8 28
#> 5 0 1993 57.1 18.8 3 SWE 8 1993:3… 1993… 43G8 28
#> 6 0 1993 57.2 18.9 3 SWE 8 1993:3… 1993… 43G8 28
#> 7 0 1993 57.3 17.9 3 SWE 8 1993:3… 1993… 43G7 27
#> 8 0 1993 57.4 16.9 3 SWE 8 1993:3… 1993… 43G6 27
#> 9 0 1993 57.4 19.2 3 SWE 8 1993:3… 1993… 43G9 28
#> 10 0 1993 57.5 17.1 3 SWE 8 1993:3… 1993… 43G7 27
#> # … with 1,124 more rows, 4 more variables: length_cm <dbl>,
#> # id_haul_stomach <chr>, species <chr>, tot_dens <dbl>, and abbreviated
#> # variable name ¹ices_rect
# Create a data frame with all combinations of trawl IDs and lengths
ex_df <- data.frame(expand.grid(
length_cm = seq_range(datcod$length_cm, by = 1),
haul.id = unique(datcod$haul.id))
)
# Create an ID that is haul + length
ex_df$haul.id.size <- paste(ex_df$haul.id, ex_df$length_cm, sep = ".")
datcod$haul.id.size <- paste(datcod$haul.id, datcod$length_cm, sep = ".")
# Remove IDs that are already in datcod
ex_df <- ex_df %>% filter(!haul.id.size %in% unique(datcod$haul.id.size))
#> filter: removed 219,790 rows (18%), 979,954 rows remaining
# Add in the other columns besides density and length
dat_for_join <- datcod %>% dplyr::select(-density, -length_cm, -haul.id.size) %>% distinct(haul.id, .keep_all = TRUE)
#> distinct: removed 210,417 rows (96%), 9,373 rows remaining
ex_df <- left_join(ex_df, dat_for_join, by = "haul.id")
#> left_join: added 11 columns (year, lat, lon, quarter, Country, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 979,954
#> > =========
#> > rows total 979,954
datcod %>% filter(haul.id.size %in% ex_df$haul.id.size)
#> filter: removed all rows (100%)
#> # A tibble: 0 × 15
#> # … with 15 variables: density <dbl>, year <int>, lat <dbl>, lon <dbl>,
#> # quarter <int>, Country <chr>, Month <int>, haul.id <chr>, IDx <chr>,
#> # ices_rect <chr>, sub_div <chr>, length_cm <dbl>, id_haul_stomach <chr>,
#> # species <chr>, haul.id.size <chr>
# Bind_rows these data with datcod
nrow(datcod) + nrow(ex_df)
#> [1] 1199744
unique(is.na(datcod$density))
#> [1] FALSE
datcod <- bind_rows(datcod, ex_df) %>% arrange(haul.id, length_cm)
nrow(datcod)
#> [1] 1199744
datcod
#> # A tibble: 1,199,744 × 15
#> density year lat lon quarter Country Month haul.id IDx ices_…¹ sub_div
#> <dbl> <int> <dbl> <dbl> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 0 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 2 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 3 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 4 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 5 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 6 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 7 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 8 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 9 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 10 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> # … with 1,199,734 more rows, 4 more variables: length_cm <dbl>,
#> # id_haul_stomach <chr>, species <chr>, haul.id.size <chr>, and abbreviated
#> # variable name ¹ices_rect
# Replace NA density with 0 density because that's the added length-classes not previously in the catch data
datcod <- datcod %>% mutate(density = replace_na(density, 0))
#> mutate: changed 979,954 values (82%) of 'density' (979954 fewer NA)
# Check the proportion zeroes are still correct:
t <- datcod %>%
group_by(haul.id) %>%
summarise(haul_density = sum(density)) %>%
ungroup()
#> group_by: one grouping variable (haul.id)
#> summarise: now 9,373 rows and 2 columns, ungrouped
#> ungroup: no grouping variables
nrow(datcod)
#> [1] 1199744
length(unique(datcod$haul.id))
#> [1] 9373
nrow(t)
#> [1] 9373
t %>% filter(!haul_density == 0)
#> filter: removed 1,134 rows (12%), 8,239 rows remaining
#> # A tibble: 8,239 × 2
#> haul.id haul_density
#> <chr> <dbl>
#> 1 1993:1:GFR:SOL:H20:21:1 78.6
#> 2 1993:1:GFR:SOL:H20:22:32 5.13
#> 3 1993:1:GFR:SOL:H20:23:31 2.62
#> 4 1993:1:GFR:SOL:H20:24:30 9.71
#> 5 1993:1:GFR:SOL:H20:25:2 405.
#> 6 1993:1:GFR:SOL:H20:26:3 181.
#> 7 1993:1:GFR:SOL:H20:27:27 11.6
#> 8 1993:1:GFR:SOL:H20:28:24 1151.
#> 9 1993:1:GFR:SOL:H20:29:29 15.4
#> 10 1993:1:GFR:SOL:H20:30:28 3.42
#> # … with 8,229 more rows
# Now do flounder
# Create a data frame with all combinations of trawl IDs and lengths
ex_df <- data.frame(expand.grid(
length_cm = seq_range(datfle$length_cm, by = 1),
haul.id = unique(datfle$haul.id))
)
# Create an ID that is haul + length
ex_df$haul.id.size <- paste(ex_df$haul.id, ex_df$length_cm, sep = ".")
datfle$haul.id.size <- paste(datfle$haul.id, datfle$length_cm, sep = ".")
# Remove IDs that are already in datfle
ex_df <- ex_df %>% filter(!haul.id.size %in% unique(datfle$haul.id.size))
#> filter: removed 111,868 rows (20%), 446,343 rows remaining
# Add in the other columns besides density and length
dat_for_join <- datfle %>% dplyr::select(-density, -length_cm, -haul.id.size) %>% distinct(haul.id, .keep_all = TRUE)
#> distinct: removed 104,371 rows (92%), 9,151 rows remaining
ex_df <- left_join(ex_df, dat_for_join, by = "haul.id")
#> left_join: added 11 columns (year, lat, lon, quarter, Country, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 446,343
#> > =========
#> > rows total 446,343
datfle %>% filter(haul.id.size %in% ex_df$haul.id.size)
#> filter: removed all rows (100%)
#> # A tibble: 0 × 15
#> # … with 15 variables: density <dbl>, year <int>, lat <dbl>, lon <dbl>,
#> # quarter <int>, Country <chr>, Month <int>, haul.id <chr>, IDx <chr>,
#> # ices_rect <chr>, sub_div <chr>, length_cm <dbl>, id_haul_stomach <chr>,
#> # species <chr>, haul.id.size <chr>
# Bind_rows these data with datfle
nrow(datfle) + nrow(ex_df)
#> [1] 559865
unique(is.na(datfle$density))
#> [1] FALSE
datfle <- bind_rows(datfle, ex_df) %>% arrange(haul.id, length_cm)
nrow(datfle)
#> [1] 559865
datfle
#> # A tibble: 559,865 × 15
#> density year lat lon quarter Country Month haul.id IDx ices_…¹ sub_div
#> <dbl> <int> <dbl> <dbl> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 0 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 2 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 3 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 4 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 5 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 6 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 7 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 8 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 9 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> 10 NA 1993 54.7 13 1 GFR 2 1993:1… 1993… 38G3 24
#> # … with 559,855 more rows, 4 more variables: length_cm <dbl>,
#> # id_haul_stomach <chr>, species <chr>, haul.id.size <chr>, and abbreviated
#> # variable name ¹ices_rect
# Replace NA density with 0 density because that's the added length-classes not previously in the catch data
datfle <- datfle %>% mutate(density = replace_na(density, 0))
#> mutate: changed 446,343 values (80%) of 'density' (446343 fewer NA)
# Merge cod and flounder data!
dat <- bind_rows(datcod, datfle)
glimpse(dat)
#> Rows: 1,759,609
#> Columns: 15
#> $ density <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000,…
#> $ year <int> 1993, 1993, 1993, 1993, 1993, 1993, 1993, 1993, 1993, …
#> $ lat <dbl> 54.6833, 54.6833, 54.6833, 54.6833, 54.6833, 54.6833, …
#> $ lon <dbl> 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13…
#> $ quarter <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ Country <chr> "GFR", "GFR", "GFR", "GFR", "GFR", "GFR", "GFR", "GFR"…
#> $ Month <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
#> $ haul.id <chr> "1993:1:GFR:SOL:H20:21:1", "1993:1:GFR:SOL:H20:21:1", …
#> $ IDx <chr> "1993.1.GFR.06S1.H20.21.1", "1993.1.GFR.06S1.H20.21.1"…
#> $ ices_rect <chr> "38G3", "38G3", "38G3", "38G3", "38G3", "38G3", "38G3"…
#> $ sub_div <chr> "24", "24", "24", "24", "24", "24", "24", "24", "24", …
#> $ length_cm <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …
#> $ id_haul_stomach <chr> NA, "1993.1.2.GFR.38G3.1", "1993.1.2.GFR.38G3.1", "199…
#> $ species <chr> "cod", "cod", "cod", "cod", "cod", "cod", "cod", "cod"…
#> $ haul.id.size <chr> "1993:1:GFR:SOL:H20:21:1.0", "1993:1:GFR:SOL:H20:21:1.…
# Check proportion zeroes
dat %>%
filter(species == "cod") %>%
group_by(haul.id) %>%
summarise(haul_dens = sum(density)) %>%
ungroup() %>%
filter(!haul_dens == 0)
#> filter: removed 559,865 rows (32%), 1,199,744 rows remaining
#> group_by: one grouping variable (haul.id)
#> summarise: now 9,373 rows and 2 columns, ungrouped
#> ungroup: no grouping variables
#> filter: removed 1,134 rows (12%), 8,239 rows remaining
#> # A tibble: 8,239 × 2
#> haul.id haul_dens
#> <chr> <dbl>
#> 1 1993:1:GFR:SOL:H20:21:1 78.6
#> 2 1993:1:GFR:SOL:H20:22:32 5.13
#> 3 1993:1:GFR:SOL:H20:23:31 2.62
#> 4 1993:1:GFR:SOL:H20:24:30 9.71
#> 5 1993:1:GFR:SOL:H20:25:2 405.
#> 6 1993:1:GFR:SOL:H20:26:3 181.
#> 7 1993:1:GFR:SOL:H20:27:27 11.6
#> 8 1993:1:GFR:SOL:H20:28:24 1151.
#> 9 1993:1:GFR:SOL:H20:29:29 15.4
#> 10 1993:1:GFR:SOL:H20:30:28 3.42
#> # … with 8,229 more rows
codq4 <- dat %>%
filter(species == "cod" & quarter == 4) %>%
group_by(haul.id) %>%
mutate(haul_dens = sum(density)) %>%
distinct(haul.id, .keep_all = TRUE) %>%
mutate(zero_catch = ifelse(haul_dens == 0, "Y", "N")) %>%
group_by(year, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_z = Y / (N+Y),
q = 4,
species = "cod")
#> filter: removed 1,290,361 rows (73%), 469,248 rows remaining
#> group_by: one grouping variable (haul.id)
#> mutate (grouped): new variable 'haul_dens' (double) with 3,260 unique values and 0% NA
#> distinct (grouped): removed 465,582 rows (99%), 3,666 rows remaining
#> mutate (grouped): new variable 'zero_catch' (character) with 2 unique values and 0% NA
#> group_by: 2 grouping variables (year, zero_catch)
#> summarise: now 56 rows and 3 columns, one group variable remaining (year)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (zero_catch, n) into (N, Y) [was 56x3, now 28x3]
#> mutate: new variable 'prop_z' (double) with 27 unique values and 0% NA
#> new variable 'q' (double) with one unique value and 0% NA
#> new variable 'species' (character) with one unique value and 0% NA
codq1 <- dat %>%
filter(species == "cod" & quarter == 1) %>%
group_by(haul.id) %>%
mutate(haul_dens = sum(density)) %>%
distinct(haul.id, .keep_all = TRUE) %>%
mutate(zero_catch = ifelse(haul_dens == 0, "Y", "N")) %>%
group_by(year, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_z = Y / (N+Y),
q = 1,
species = "cod")
#> filter: removed 1,043,449 rows (59%), 716,160 rows remaining
#> group_by: one grouping variable (haul.id)
#> mutate (grouped): new variable 'haul_dens' (double) with 4,968 unique values and 0% NA
#> distinct (grouped): removed 710,565 rows (99%), 5,595 rows remaining
#> mutate (grouped): new variable 'zero_catch' (character) with 2 unique values and 0% NA
#> group_by: 2 grouping variables (year, zero_catch)
#> summarise: now 56 rows and 3 columns, one group variable remaining (year)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (zero_catch, n) into (N, Y) [was 56x3, now 28x3]
#> mutate: new variable 'prop_z' (double) with 28 unique values and 0% NA
#> new variable 'q' (double) with one unique value and 0% NA
#> new variable 'species' (character) with one unique value and 0% NA
fleq4 <- dat %>%
filter(species == "flounder" & quarter == 4) %>%
group_by(haul.id) %>%
mutate(haul_dens = sum(density)) %>%
distinct(haul.id, .keep_all = TRUE) %>%
mutate(zero_catch = ifelse(haul_dens == 0, "Y", "N")) %>%
group_by(year, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_z = Y / (N+Y),
q = 4,
species = "flounder")
#> filter: removed 1,535,795 rows (87%), 223,814 rows remaining
#> group_by: one grouping variable (haul.id)
#> mutate (grouped): new variable 'haul_dens' (double) with 2,983 unique values and 0% NA
#> distinct (grouped): removed 220,157 rows (98%), 3,657 rows remaining
#> mutate (grouped): new variable 'zero_catch' (character) with 2 unique values and 0% NA
#> group_by: 2 grouping variables (year, zero_catch)
#> summarise: now 55 rows and 3 columns, one group variable remaining (year)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (zero_catch, n) into (N, Y) [was 55x3, now 28x3]
#> mutate: new variable 'prop_z' (double) with 27 unique values and 4% NA
#> new variable 'q' (double) with one unique value and 0% NA
#> new variable 'species' (character) with one unique value and 0% NA
fleq1 <- dat %>%
filter(species == "flounder" & quarter == 1) %>%
group_by(haul.id) %>%
mutate(haul_dens = sum(density)) %>%
distinct(haul.id, .keep_all = TRUE) %>%
mutate(zero_catch = ifelse(haul_dens == 0, "Y", "N")) %>%
group_by(year, zero_catch) %>%
summarise(n = n()) %>%
ungroup() %>%
pivot_wider(names_from = zero_catch, values_from = n) %>%
mutate(prop_z = Y / (N+Y),
q = 1,
species = "flounder")
#> filter: removed 1,430,390 rows (81%), 329,219 rows remaining
#> group_by: one grouping variable (haul.id)
#> mutate (grouped): new variable 'haul_dens' (double) with 5,026 unique values and 0% NA
#> distinct (grouped): removed 323,837 rows (98%), 5,382 rows remaining
#> mutate (grouped): new variable 'zero_catch' (character) with 2 unique values and 0% NA
#> group_by: 2 grouping variables (year, zero_catch)
#> summarise: now 56 rows and 3 columns, one group variable remaining (year)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (zero_catch, n) into (N, Y) [was 56x3, now 28x3]
#> mutate: new variable 'prop_z' (double) with 28 unique values and 0% NA
#> new variable 'q' (double) with one unique value and 0% NA
#> new variable 'species' (character) with one unique value and 0% NA
ggplot(bind_rows(codq1, codq4, fleq1, fleq4), aes(year, prop_z*100, color = factor(q))) +
geom_line() +
facet_wrap(~ species, ncol = 1)
# Save the full catch data as dat_full. For adding the unique covariates, I only need the distinct trawl IDs, not the 1.7 million rows...
dat_full <- dat
dat <- dat %>% dplyr::select(-density, length_cm, species, haul.id.size) %>% distinct(haul.id, .keep_all = TRUE)
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
# Check 0 catches
dat_full %>%
filter(species == "cod") %>%
group_by(haul.id) %>%
summarise(haul_dens = sum(density)) %>%
ungroup() %>%
filter(!haul_dens == 0)
#> filter: removed 559,865 rows (32%), 1,199,744 rows remaining
#> group_by: one grouping variable (haul.id)
#> summarise: now 9,373 rows and 2 columns, ungrouped
#> ungroup: no grouping variables
#> filter: removed 1,134 rows (12%), 8,239 rows remaining
#> # A tibble: 8,239 × 2
#> haul.id haul_dens
#> <chr> <dbl>
#> 1 1993:1:GFR:SOL:H20:21:1 78.6
#> 2 1993:1:GFR:SOL:H20:22:32 5.13
#> 3 1993:1:GFR:SOL:H20:23:31 2.62
#> 4 1993:1:GFR:SOL:H20:24:30 9.71
#> 5 1993:1:GFR:SOL:H20:25:2 405.
#> 6 1993:1:GFR:SOL:H20:26:3 181.
#> 7 1993:1:GFR:SOL:H20:27:27 11.6
#> 8 1993:1:GFR:SOL:H20:28:24 1151.
#> 9 1993:1:GFR:SOL:H20:29:29 15.4
#> 10 1993:1:GFR:SOL:H20:30:28 3.42
#> # … with 8,229 more rowsAdd in the environmental variables
Substrate
substrate <- raster("data/substrate_tif/BALANCE_SEABED_SEDIMENT.tif")
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
substrate_longlat = projectRaster(substrate, crs = ('+proj=longlat'))
# Now extract the values from the saduria raster to dat
dat$substrate <- extract(substrate_longlat, dat %>% dplyr::select(lon, lat))
unique(dat$substrate)
#> [1] 3.000000 5.000000 4.000000 1.138341 4.446301 2.000000 3.734845 1.000000
#> [9] 4.066592 3.807718 4.438934 4.842565 4.798625 4.840808 4.379877 3.512073
#> [17] 4.165947 4.706898 1.710033 4.659087 4.950544 3.852555 4.155707 4.992289
#> [25] 2.727517 3.719923 4.909709 2.064201 4.238063 4.498091 3.251222 4.843821
#> [33] 2.055089 1.402886 3.429073 4.639466 4.752425 4.038259 2.716163 3.078310
#> [41] 4.169430 3.024738 2.007402 4.957880 3.838716 3.486158 3.243755 2.137971
#> [49] 3.622974 2.194785 4.814487 4.018015 4.415854 4.834043 4.054638 4.302037
#> [57] 4.412596 3.541952 3.573007 4.943159 2.985540 4.102325 4.102802 4.582128
#> [65] 4.168352 4.367814 4.528939 4.609104 4.982928 2.837596 3.011781 4.468152
#> [73] 2.205551 2.720424 3.362097 3.161689 4.246033 2.199707 3.268652 2.867899
#> [81] 3.313120 2.384691 3.727420 4.978446 3.843230 4.533843 3.536023 2.308508
#> [89] 3.376948 4.676910 2.593988 3.781663 4.239673 3.902180 4.490885 4.648592
#> [97] 3.349005 4.041116 1.412668 3.606772 3.029200 4.536513 2.296147 3.851118
#> [105] 2.127370 2.571771 3.777866 2.899165 3.694996 3.810013 2.463042 3.845765
#> [113] 2.370193 4.636803 4.278285 4.131986 2.738054 2.370191 2.015659 4.899457
#> [121] 2.856868 4.155565 4.812246 3.535448 2.571619 4.209975 3.443494 3.032781
#> [129] 3.927895 4.980990 3.861495 4.014337 3.928918 2.892701 4.286916 4.202067
#> [137] 4.819160 2.843091 3.753357 2.009381 3.247610 4.384879 2.753052 2.367529
#> [145] 3.372948 4.708170 3.596280 3.048587 2.152307 2.986556 4.651357 3.666669
#> [153] 2.676910 4.866229 3.974051 3.906186 3.112969 3.538048 4.910212 4.506713
#> [161] 2.229670 2.962793 3.628756 3.067581 4.227833 3.424838 4.620548 2.791879
#> [169] 4.778975 4.952352 2.033904 3.983402 2.918824 4.493097 2.271779 4.119308
#> [177] 4.996012 2.588547 4.051414 3.318623 3.480814 4.873419 4.355332 3.508960
#> [185] 3.797020 4.127856 4.470470 4.457530 3.552718 3.438254 4.525368 4.900706
#> [193] 2.044159 4.212301 4.011298 3.523882 3.060409 4.991976 3.245151
# Plot
ggplot(dat, aes(lon, lat, color = substrate)) +
geom_point()
factor(sort(unique(round(dat$substrate))))
#> [1] 1 2 3 4 5
#> Levels: 1 2 3 4 5
dat$substrate <- round(dat$substrate)
dat <- dat %>% mutate(substrate = ifelse(substrate == 1, "bedrock", substrate),
substrate = ifelse(substrate == 2, "hard-bottom complex", substrate),
substrate = ifelse(substrate == 3, "sand", substrate),
substrate = ifelse(substrate == 4, "hard clay", substrate),
substrate = ifelse(substrate == 5, "mud", substrate))
#> mutate: converted 'substrate' from double to character (0 new NA)
# I. Bedrock.
# II. Hard bottom complex, includes patchy hard surfaces and coarse sand (sometimes also clay) to boulders.
# III. Sand including fine to coarse sand (with gravel exposures).
# IV. Hard clay sometimes/often/possibly exposed or covered with a thin layer of
# sand/gravel.
# V. Mud including gyttja-clay to gyttja-silt.
# Plot
ggplot(dat, aes(lon, lat, color = substrate)) +
geom_point()
Depth
# Read the tifs
west <- raster("data/depth_geo_tif/D5_2018_rgb-1.tif")
#plot(west)
east <- raster("data/depth_geo_tif/D6_2018_rgb-1.tif")
# plot(east)
dep_rast <- raster::merge(west, east)
dat$depth <- extract(dep_rast, dat %>% dplyr::select(lon, lat))
# Convert to depth (instead of elevation)
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dat$depth <- (dat$depth - max(dat$depth)) *-1
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Oxygen
# Downloaded from here: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_BIO_003_012
# Extract raster points: https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
# https://rpubs.com/boyerag/297592
# https://pjbartlein.github.io/REarthSysSci/netCDF.html#get-a-variable
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1664182224542.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1664182224542.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float o2b[longitude,latitude,time]
#> long_name: Sea_floor_Dissolved_Oxygen_Concentration
#> missing_value: NaN
#> standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
#> units: mmol m-3
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:336
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25917.5
#> latitude Size:523
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 48.49169921875
#> valid_max: 65.8914184570312
#> longitude Size:383
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 30.2357654571533
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_BIO_003_012
#> title: CMEMS V4 Reanalysis: SCOBI model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20201201_20201201
#> file_quality_index: 1
#> creation_date: 2021-11-09 UTC
#> bullentin_date: 20201201
#> start_date: 2020-12-01 UTC
#> stop_date: 2020-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 48.49170 48.52503 48.55836 48.59170 48.62503 48.65836
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
#> [325] 25582.5 25612.5 25642.5 25673.0 25703.5 25734.0 25764.5 25795.5 25826.0
#> [334] 25856.5 25887.0 25917.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 336
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)
#> [1] 383 523 336
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
# Next, we need to work with the months that correspond to the quarters that we use.
# loop through each time step, and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [326] 2 3 4 5 6 7 8 9 10 11 12
index_keep_q1 <- which(months < 4)
index_keep_q4 <- which(months > 9)
oxy_q1 <- oxy_array[, , index_keep_q1]
oxy_q4 <- oxy_array[, , index_keep_q4]
months_keep_q1 <- months[index_keep_q1]
months_keep_q4 <- months[index_keep_q4]
years_keep_q1 <- years[index_keep_q1]
years_keep_q4 <- years[index_keep_q4]
# Now we have an array with data for that quarter
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq_q1 <- seq(1, dim(oxy_q1)[3], by = 3)
loop_seq_q4 <- seq(1, dim(oxy_q4)[3], by = 3)
# Create objects that will hold data
dlist_q1 <- list()
dlist_q4 <- list()
oxy_1 <- c()
oxy_2 <- c()
oxy_3 <- c()
oxy_ave_q1 <- c()
oxy_10 <- c()
oxy_11 <- c()
oxy_12 <- c()
oxy_ave_q4 <- c()
# Now average by quarter. The vector loop_seq_q1 is 1, 4, 7 etc. So first i is 1, 2, 3,
# which is the index we want.
for(i in loop_seq_q1) { # We can use q1 as looping index, doesn't matter!
oxy_1 <- oxy_q1[, , (i)]
oxy_2 <- oxy_q1[, , (i + 1)]
oxy_3 <- oxy_q1[, , (i + 2)]
oxy_10 <- oxy_q4[, , (i)]
oxy_11 <- oxy_q4[, , (i + 1)]
oxy_12 <- oxy_q4[, , (i + 2)]
oxy_ave_q1 <- (oxy_1 + oxy_2 + oxy_3) / 3
oxy_ave_q4 <- (oxy_10 + oxy_11 + oxy_12) / 3
list_pos_q1 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
list_pos_q4 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist_q1[[list_pos_q1]] <- oxy_ave_q1
dlist_q4[[list_pos_q4]] <- oxy_ave_q4
}
# Now name the lists with the year:
names(dlist_q1) <- unique(years_keep_q1)
names(dlist_q4) <- unique(years_keep_q4)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have oxygen for
d_sub_oxy_q1 <- dat %>% filter(quarter == 1) %>% filter(year %in% names(dlist_q1)) %>% droplevels()
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
#> filter: no rows removed
d_sub_oxy_q4 <- dat %>% filter(quarter == 4) %>% filter(year %in% names(dlist_q4)) %>% droplevels()
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
#> filter: no rows removed
# Create data holding object
oxy_data_list_q1 <- list()
oxy_data_list_q4 <- list()
# ... And for the oxygen raster
raster_list_q1 <- list()
raster_list_q4 <- list()
# Create factor year for indexing the list in the loop
d_sub_oxy_q1$year_f <- as.factor(d_sub_oxy_q1$year)
d_sub_oxy_q4$year_f <- as.factor(d_sub_oxy_q4$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_oxy_q1$year_f)) { # We can use q1 as looping index, doesn't matter!
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a year
oxy_slice_q1 <- dlist_q1[[i]]
oxy_slice_q4 <- dlist_q4[[i]]
# Create raster for that year (i)
r_q1 <- raster(t(oxy_slice_q1), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
r_q4 <- raster(t(oxy_slice_q4), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r_q1 <- flip(r_q1, direction = 'y')
r_q4 <- flip(r_q4, direction = 'y')
plot(r_q1, main = paste(i, "Q1"))
plot(r_q4, main = paste(i, "Q4"))
# Filter the same year (i) in the cpue data and select only coordinates
d_slice_q1 <- d_sub_oxy_q1 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
d_slice_q4 <- d_sub_oxy_q4 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp_q1 <- SpatialPoints(d_slice_q1)
data_sp_q4 <- SpatialPoints(d_slice_q4)
# Extract raster value (oxygen)
rasValue_q1 <- raster::extract(r_q1, data_sp_q1)
rasValue_q4 <- raster::extract(r_q4, data_sp_q4)
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for plot)
df_q1 <- as.data.frame(data_sp_q1)
df_q4 <- as.data.frame(data_sp_q4)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice_q1$oxy <- rasValue_q1
d_slice_q4$oxy <- rasValue_q4
# Add in which year
d_slice_q1$year <- i
d_slice_q4$year <- i
# Now the unit of oxygen is mmol/m3. I want it to be ml/L. The original model is in unit ml/L
# and it's been converted by the data host. Since it was converted without accounting for
# pressure or temperature, I can simply use the following conversion factor:
# 1 ml/l = 103/22.391 = 44.661 μmol/l -> 1 ml/l = 0.044661 mmol/l = 44.661 mmol/m^3 -> 0.0223909 ml/l = 1mmol/m^3
# https://ocean.ices.dk/tools/unitconversion.aspx
d_slice_q1$oxy <- d_slice_q1$oxy * 0.0223909
d_slice_q4$oxy <- d_slice_q4$oxy * 0.0223909
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index_q1 <- as.numeric(as.character(d_slice_q1$year))[1] - 1992
index_q4 <- as.numeric(as.character(d_slice_q4$year))[1] - 1992
# Add each years' data in the list
oxy_data_list_q1[[index_q1]] <- d_slice_q1
oxy_data_list_q4[[index_q4]] <- d_slice_q4
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map_q1 <- rasterToPoints(r_q1)
map_q4 <- rasterToPoints(r_q4)
# Make the points a dataframe for ggplot
df_rast_q1 <- data.frame(map_q1)
df_rast_q4 <- data.frame(map_q4)
# Rename y-variable and add year
df_rast_q1 <- df_rast_q1 %>% rename("oxy" = "layer") %>% mutate(year = i)
df_rast_q4 <- df_rast_q4 %>% rename("oxy" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list_q1[[index_q1]] <- df_rast_q1
raster_list_q4[[index_q4]] <- df_rast_q4
# Make appropriate column headings
colnames(df_rast_q1) <- c("Longitude", "Latitude", "oxy")
colnames(df_rast_q4) <- c("Longitude", "Latitude", "oxy")
# Change unit to ml/L
df_rast_q1$oxy <- df_rast_q1$oxy * 0.0223909
df_rast_q4$oxy <- df_rast_q4$oxy * 0.0223909
# Make a map for q1
ggplot(data = df_rast_q1, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = oxy)) +
geom_point(data = d_slice_q1, aes(x = lon, y = lat, fill = oxy),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-5, 11)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-5, 11)) +
NULL
ggsave(paste("figures/supp/cpue_oxygen_rasters/", i,"q1.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
# Make a map for q4
ggplot(data = df_rast_q4, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = oxy)) +
geom_point(data = d_slice_q4, aes(x = lon, y = lat, fill = oxy),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-5, 11)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-5, 11)) +
NULL
ggsave(paste("figures/supp/cpue_oxygen_rasters/", i,"q4.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 5,494 rows (98%), 101 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,420 rows (97%), 175 rows remaining
#> filter: removed 3,604 rows (98%), 62 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,441 rows (97%), 154 rows remaining
#> filter: removed 3,613 rows (99%), 53 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,412 rows (97%), 183 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,381 rows (96%), 214 rows remaining
#> filter: removed 3,591 rows (98%), 75 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,367 rows (96%), 228 rows remaining
#> filter: removed 3,598 rows (98%), 68 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,405 rows (97%), 190 rows remaining
#> filter: removed 3,572 rows (97%), 94 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,485 rows (98%), 110 rows remaining
#> filter: removed 3,577 rows (98%), 89 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,550 rows (97%), 116 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,450 rows (97%), 145 rows remaining
#> filter: removed 3,549 rows (97%), 117 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,434 rows (97%), 161 rows remaining
#> filter: removed 3,538 rows (97%), 128 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,388 rows (96%), 207 rows remaining
#> filter: removed 3,557 rows (97%), 109 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,377 rows (96%), 218 rows remaining
#> filter: removed 3,515 rows (96%), 151 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,407 rows (97%), 188 rows remaining
#> filter: removed 3,516 rows (96%), 150 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,360 rows (96%), 235 rows remaining
#> filter: removed 3,497 rows (95%), 169 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,373 rows (96%), 222 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,336 rows (95%), 259 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,355 rows (96%), 240 rows remaining
#> filter: removed 3,504 rows (96%), 162 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,352 rows (96%), 243 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,371 rows (96%), 224 rows remaining
#> filter: removed 3,533 rows (96%), 133 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,327 rows (95%), 268 rows remaining
#> filter: removed 3,518 rows (96%), 148 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,358 rows (96%), 237 rows remaining
#> filter: removed 3,500 rows (95%), 166 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,363 rows (96%), 232 rows remaining
#> filter: removed 3,453 rows (94%), 213 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,350 rows (96%), 245 rows remaining
#> filter: removed 3,441 rows (94%), 225 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,339 rows (95%), 256 rows remaining
#> filter: removed 3,456 rows (94%), 210 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,477 rows (98%), 118 rows remaining
#> filter: removed 3,574 rows (97%), 92 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,469 rows (98%), 126 rows remaining
#> filter: removed 3,564 rows (97%), 102 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_oxy_q1 <- dplyr::bind_rows(oxy_data_list_q1)
big_dat_oxy_q4 <- dplyr::bind_rows(oxy_data_list_q4)
big_dat_oxy <- bind_rows(mutate(big_dat_oxy_q1, quarter = 1),
mutate(big_dat_oxy_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
big_raster_dat_oxy_q1 <- dplyr::bind_rows(raster_list_q1)
big_raster_dat_oxy_q4 <- dplyr::bind_rows(raster_list_q4)
big_raster_dat_oxy <- bind_rows(mutate(big_raster_dat_oxy_q1, quarter = 1),
mutate(big_raster_dat_oxy_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
# Plot data, looks like there's big inter-annual variation but a negative trend over time
big_raster_dat_oxy %>%
group_by(quarter, year) %>%
drop_na(oxy) %>%
summarise(mean_oxy = mean(oxy)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, mean_oxy)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter) +
NULL
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> summarise: now 56 rows and 3 columns, one group variable remaining (quarter)
#> mutate (grouped): new variable 'year_num' (double) with 28 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
big_raster_dat_oxy %>%
group_by(quarter, year) %>%
drop_na(oxy) %>%
mutate(dead = ifelse(oxy < 0, "Y", "N")) %>%
filter(dead == "Y") %>%
mutate(n = n(),
year_num = as.numeric(year)) %>%
ggplot(., aes(year_num, n)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter) +
NULL
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> mutate (grouped): new variable 'dead' (character) with 2 unique values and 0% NA
#> filter (grouped): removed 1,873,405 rows (96%), 84,075 rows remaining
#> mutate (grouped): new variable 'n' (integer) with 56 unique values and 0% NA
#> new variable 'year_num' (double) with 28 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new oxygen column in the original data:
str(d_sub_oxy_q1)
#> tibble [5,595 × 17] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:5595] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:5595] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:5595] 13 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:5595] 1 1 1 1 1 1 1 1 1 1 ...
#> $ Country : chr [1:5595] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:5595] 2 2 2 2 2 2 2 2 2 2 ...
#> $ haul.id : chr [1:5595] "1993:1:GFR:SOL:H20:21:1" "1993:1:GFR:SOL:H20:22:32" "1993:1:GFR:SOL:H20:23:31" "1993:1:GFR:SOL:H20:24:30" ...
#> $ IDx : chr [1:5595] "1993.1.GFR.06S1.H20.21.1" "1993.1.GFR.06S1.H20.22.32" "1993.1.GFR.06S1.H20.23.31" "1993.1.GFR.06S1.H20.24.30" ...
#> $ ices_rect : chr [1:5595] "38G3" "38G4" "38G4" "37G3" ...
#> $ sub_div : chr [1:5595] "24" "24" "24" "24" ...
#> $ length_cm : num [1:5595] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:5595] NA NA NA NA ...
#> $ species : chr [1:5595] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:5595] "1993:1:GFR:SOL:H20:21:1.0" "1993:1:GFR:SOL:H20:22:32.0" "1993:1:GFR:SOL:H20:23:31.0" "1993:1:GFR:SOL:H20:24:30.0" ...
#> $ substrate : chr [1:5595] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:5595] 9 7 8 6 13 15 11 15 10 10 ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(d_sub_oxy_q4)
#> tibble [3,666 × 17] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:3666] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:3666] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:3666] 13.1 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:3666] 4 4 4 4 4 4 4 4 4 4 ...
#> $ Country : chr [1:3666] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:3666] 12 11 11 11 12 12 11 11 11 11 ...
#> $ haul.id : chr [1:3666] "1993:4:GFR:SOL:H20:21:65" "1993:4:GFR:SOL:H20:22:43" "1993:4:GFR:SOL:H20:23:44" "1993:4:GFR:SOL:H20:24:46" ...
#> $ IDx : chr [1:3666] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" "1993.4.GFR.06S1.H20.23.44" "1993.4.GFR.06S1.H20.24.46" ...
#> $ ices_rect : chr [1:3666] "38G3" "38G4" "38G4" "37G4" ...
#> $ sub_div : chr [1:3666] "24" "24" "24" "24" ...
#> $ length_cm : num [1:3666] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:3666] NA NA NA NA ...
#> $ species : chr [1:3666] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:3666] "1993:4:GFR:SOL:H20:21:65.0" "1993:4:GFR:SOL:H20:22:43.0" "1993:4:GFR:SOL:H20:23:44.0" "1993:4:GFR:SOL:H20:24:46.0" ...
#> $ substrate : chr [1:3666] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:3666] 8 7 8 7 16 17 11 18 10 10 ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_oxy)
#> tibble [9,261 × 5] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:9261] 13 14.3 14.2 14 13.1 ...
#> $ lat : num [1:9261] 54.7 54.5 54.5 54.5 54.7 ...
#> $ oxy : num [1:9261] 8.49 8.89 8.9 8.89 8.45 ...
#> $ year : chr [1:9261] "1993" "1993" "1993" "1993" ...
#> $ quarter: num [1:9261] 1 1 1 1 1 1 1 1 1 1 ...
# Create an ID for matching the oxygen data with the cpue data
dat$id_oxy <- paste(dat$year, dat$quarter, dat$lon, dat$lat, sep = "_")
big_dat_oxy$id_oxy <- paste(big_dat_oxy$year, big_dat_oxy$quarter, big_dat_oxy$lon, big_dat_oxy$lat, sep = "_")
# Which id's are NOT in the cpue data (dat)?
head(dat$id_oxy, 100)
#> [1] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167"
#> [4] "1993_1_13.9833_54.4833" "1993_1_13.0833_54.7333" "1993_1_13.1833_54.7"
#> [7] "1993_1_13.8_54.55" "1993_1_14.1167_54.7" "1993_1_14.0833_54.5833"
#> [10] "1993_1_13.8667_54.5833" "1993_1_14.15_54.6333" "1993_1_13.3667_54.7333"
#> [13] "1993_1_13.5167_54.7333" "1993_1_13.6333_54.6833" "1993_1_13.7333_54.6667"
#> [16] "1993_1_13.85_54.7" "1993_1_13.6667_54.7667" "1993_1_14.0167_54.85"
#> [19] "1993_1_13.8833_54.9667" "1993_1_13.6167_54.9167" "1993_1_13.2167_54.8667"
#> [22] "1993_1_13.1_54.9333" "1993_1_13.3167_54.9833" "1993_1_13.4333_55.0333"
#> [25] "1993_1_13.75_55.0667" "1993_1_14.1667_55.1333" "1993_1_14.2667_55.1667"
#> [28] "1993_1_14.2833_55.0667" "1993_1_14.2333_55" "1993_1_13.6167_55.05"
#> [31] "1993_1_13.8167_55.0833" "1993_1_16.6_54.8667" "1993_1_15.1167_54.3"
#> [34] "1993_1_16.5167_54.8333" "1993_1_15.0167_54.4333" "1993_1_16.6333_54.85"
#> [37] "1993_1_15.7667_54.3667" "1993_1_16.0167_54.4333" "1993_1_17.5_55"
#> [40] "1993_1_15.3333_55.9333" "1993_1_15.7167_54.4333" "1993_1_16.2167_54.9167"
#> [43] "1993_1_16.3_55.8333" "1993_1_15.95_54.7833" "1993_1_17.6167_56.0833"
#> [46] "1993_1_15.6_55.9167" "1993_1_15.5833_54.6167" "1993_1_16.2333_55.7833"
#> [49] "1993_1_17.75_56.1333" "1993_1_15.1167_55.6833" "1993_1_16.4333_55.45"
#> [52] "1993_1_14.8667_55.6167" "1993_1_15.6_54.7667" "1993_1_16.35_55.3667"
#> [55] "1993_1_15.8833_55.6" "1993_1_15.7_55.6" "1993_1_17.35_55.2667"
#> [58] "1993_1_16.2_55.3" "1993_1_15.1667_55.4333" "1993_1_17.55_55.25"
#> [61] "1993_1_16.05_55.1333" "1993_1_17.0855_55.8667" "1993_1_17.1323_55.9248"
#> [64] "1993_1_17.7367_55.8015" "1993_1_17.7188_56.0352" "1993_1_16.9745_56.445"
#> [67] "1993_1_16.8225_56.5178" "1993_1_17.94_57.004" "1993_1_17.9145_57.066"
#> [70] "1993_1_16.9133_57.3685" "1993_1_17.0157_57.5172" "1993_1_17.5322_57.4502"
#> [73] "1993_1_18.123_57.821" "1993_1_18.3128_57.7555" "1993_1_19.4038_57.8736"
#> [76] "1993_1_19.5368_57.8885" "1993_1_19.5493_57.8677" "1993_1_19.0568_57.5685"
#> [79] "1993_1_19.1942_57.3825" "1993_1_18.8761_57.1808" "1993_1_18.8363_57.085"
#> [82] "1993_1_18.9005_57.089" "1993_1_18.4828_56.2562" "1993_1_18.3732_55.5878"
#> [85] "1993_1_16.9951_55.2882" "1993_1_16.0507_55.3282" "1993_1_15.3205_55.412"
#> [88] "1993_1_16.1217_55.795" "1993_1_16.085_55.8733" "1993_1_15.46_55.9933"
#> [91] "1993_1_15.4393_55.8218" "1993_1_14.7172_55.5687" "1993_1_14.5143_55.6712"
#> [94] "1993_1_16.611_55.6187" "1993_1_16.8935_55.785" "1993_1_14.7333_55.455"
#> [97] "1993_1_14.5483_55.465" "1993_1_13.9913_54.9983" "1993_1_13.6493_55.213"
#> [100] "1993_1_13.32_54.935"
head(big_dat_oxy$id_oxy, 100)
#> [1] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167"
#> [4] "1993_1_13.9833_54.4833" "1993_1_13.0833_54.7333" "1993_1_13.1833_54.7"
#> [7] "1993_1_13.8_54.55" "1993_1_14.1167_54.7" "1993_1_14.0833_54.5833"
#> [10] "1993_1_13.8667_54.5833" "1993_1_14.15_54.6333" "1993_1_13.3667_54.7333"
#> [13] "1993_1_13.5167_54.7333" "1993_1_13.6333_54.6833" "1993_1_13.7333_54.6667"
#> [16] "1993_1_13.85_54.7" "1993_1_13.6667_54.7667" "1993_1_14.0167_54.85"
#> [19] "1993_1_13.8833_54.9667" "1993_1_13.6167_54.9167" "1993_1_13.2167_54.8667"
#> [22] "1993_1_13.1_54.9333" "1993_1_13.3167_54.9833" "1993_1_13.4333_55.0333"
#> [25] "1993_1_13.75_55.0667" "1993_1_14.1667_55.1333" "1993_1_14.2667_55.1667"
#> [28] "1993_1_14.2833_55.0667" "1993_1_14.2333_55" "1993_1_13.6167_55.05"
#> [31] "1993_1_13.8167_55.0833" "1993_1_16.6_54.8667" "1993_1_15.1167_54.3"
#> [34] "1993_1_16.5167_54.8333" "1993_1_15.0167_54.4333" "1993_1_16.6333_54.85"
#> [37] "1993_1_15.7667_54.3667" "1993_1_16.0167_54.4333" "1993_1_17.5_55"
#> [40] "1993_1_15.3333_55.9333" "1993_1_15.7167_54.4333" "1993_1_16.2167_54.9167"
#> [43] "1993_1_16.3_55.8333" "1993_1_15.95_54.7833" "1993_1_17.6167_56.0833"
#> [46] "1993_1_15.6_55.9167" "1993_1_15.5833_54.6167" "1993_1_16.2333_55.7833"
#> [49] "1993_1_17.75_56.1333" "1993_1_15.1167_55.6833" "1993_1_16.4333_55.45"
#> [52] "1993_1_14.8667_55.6167" "1993_1_15.6_54.7667" "1993_1_16.35_55.3667"
#> [55] "1993_1_15.8833_55.6" "1993_1_15.7_55.6" "1993_1_17.35_55.2667"
#> [58] "1993_1_16.2_55.3" "1993_1_15.1667_55.4333" "1993_1_17.55_55.25"
#> [61] "1993_1_16.05_55.1333" "1993_1_17.0855_55.8667" "1993_1_17.1323_55.9248"
#> [64] "1993_1_17.7367_55.8015" "1993_1_17.7188_56.0352" "1993_1_16.9745_56.445"
#> [67] "1993_1_16.8225_56.5178" "1993_1_17.94_57.004" "1993_1_17.9145_57.066"
#> [70] "1993_1_16.9133_57.3685" "1993_1_17.0157_57.5172" "1993_1_17.5322_57.4502"
#> [73] "1993_1_18.123_57.821" "1993_1_18.3128_57.7555" "1993_1_19.4038_57.8736"
#> [76] "1993_1_19.5368_57.8885" "1993_1_19.5493_57.8677" "1993_1_19.0568_57.5685"
#> [79] "1993_1_19.1942_57.3825" "1993_1_18.8761_57.1808" "1993_1_18.8363_57.085"
#> [82] "1993_1_18.9005_57.089" "1993_1_18.4828_56.2562" "1993_1_18.3732_55.5878"
#> [85] "1993_1_16.9951_55.2882" "1993_1_16.0507_55.3282" "1993_1_15.3205_55.412"
#> [88] "1993_1_16.1217_55.795" "1993_1_16.085_55.8733" "1993_1_15.46_55.9933"
#> [91] "1993_1_15.4393_55.8218" "1993_1_14.7172_55.5687" "1993_1_14.5143_55.6712"
#> [94] "1993_1_16.611_55.6187" "1993_1_16.8935_55.785" "1993_1_14.7333_55.455"
#> [97] "1993_1_14.5483_55.465" "1993_1_13.9913_54.9983" "1993_1_13.6493_55.213"
#> [100] "1993_1_13.32_54.935"
tail(dat$id_oxy, 100)
#> [1] "2020_4_20.985_57.0167" "2020_4_21.0083_57.0233" "2020_4_20.72_56.6267"
#> [4] "2020_4_20.7083_56.6233" "2020_4_20.68_56.625" "2020_4_20.5983_56.64"
#> [7] "2020_4_20.2583_56.54" "2020_4_19.8717_56.1317" "2020_4_20.5433_57.0467"
#> [10] "2020_4_19.9117_56.17" "2020_4_20.08_56.3833" "2020_4_19.6617_56.2583"
#> [13] "2020_4_20.59_57.1717" "2020_4_20.6733_57.205" "2020_4_21.1217_57.5067"
#> [16] "2020_4_21.4283_57.4983" "2020_4_21.3433_57.41" "2020_4_21.1033_57.255"
#> [19] "2020_4_16.6483_54.8733" "2020_4_15.5817_54.3833" "2020_4_15.7833_54.38"
#> [22] "2020_4_16.005_54.4233" "2020_4_15.9917_54.405" "2020_4_16.4183_54.6567"
#> [25] "2020_4_16.6367_54.6933" "2020_4_16.8417_54.7517" "2020_4_16.8833_54.7417"
#> [28] "2020_4_17.3667_54.9517" "2020_4_17.48_54.85" "2020_4_17.3067_55.3167"
#> [31] "2020_4_17.9567_55.735" "2020_4_17.375_55.2567" "2020_4_17.3967_54.9967"
#> [34] "2020_4_17.31_55.235" "2020_4_19.1983_54.3817" "2020_4_18.1983_54.895"
#> [37] "2020_4_18.5733_54.8567" "2020_4_18.015_55.69" "2020_4_18.665_56.4567"
#> [40] "2020_4_18.6883_56.4733" "2020_4_18.96_55.1833" "2020_4_18.91_55.4417"
#> [43] "2020_4_18.9967_55.5117" "2020_4_18.6083_56.4017" "2020_4_19.0183_54.4033"
#> [46] "2020_4_18.76_54.7267" "2020_4_19.2833_54.41" "2020_4_18.535_55.485"
#> [49] "2020_4_18.6733_54.795" "2020_4_18.3567_56.1767" "2020_4_18.2233_55.5283"
#> [52] "2020_4_18.5167_54.9933" "2020_4_19.2283_54.395" "2020_4_19.0567_54.4167"
#> [55] "2020_4_19.3117_54.45" "2020_4_19.0133_54.3967" "2020_4_19.0367_54.4167"
#> [58] "2020_4_18.5033_54.8333" "2020_4_18.6067_54.915" "2020_4_18.1817_54.8983"
#> [61] "2020_4_18.93_54.525" "2020_4_18.9267_54.51" "2020_4_19.075_54.4383"
#> [64] "2020_4_19.04_54.4217" "2020_4_19.325_54.575" "2020_4_19.1717_54.6"
#> [67] "2020_4_19.1117_54.4333" "2020_4_16.9165_57.3647" "2020_4_17.0241_57.4144"
#> [70] "2020_4_17.0912_57.4622" "2020_4_17.0977_57.6175" "2020_4_18.1196_57.7804"
#> [73] "2020_4_17.5616_57.4828" "2020_4_19.1682_57.6135" "2020_4_19.3769_57.6885"
#> [76] "2020_4_19.4807_57.7685" "2020_4_19.4891_58.0354" "2020_4_19.439_58.0746"
#> [79] "2020_4_19.5044_57.876" "2020_4_19.2267_57.3361" "2020_4_18.9076_57.089"
#> [82] "2020_4_18.8636_57.1753" "2020_4_19.0668_57.2496" "2020_4_19.0591_57.3072"
#> [85] "2020_4_19.1085_57.319" "2020_4_17.9509_56.9956" "2020_4_17.9167_57.0532"
#> [88] "2020_4_17.4146_57.2805" "2020_4_17.184_56.9584" "2020_4_16.9939_56.7256"
#> [91] "2020_4_16.85_56.5655" "2020_4_13.6016_55.2125" "2020_4_13.925_55.286"
#> [94] "2020_4_13.9518_55.26" "2020_4_14.4799_55.4589" "2020_4_14.5095_55.6877"
#> [97] "2020_4_14.3632_55.6986" "2020_4_14.3795_55.7037" "2020_4_15.5667_55.883"
#> [100] "2020_4_15.5703_55.8469"
tail(big_dat_oxy$id_oxy, 100)
#> [1] "2020_4_20.985_57.0167" "2020_4_21.0083_57.0233" "2020_4_20.72_56.6267"
#> [4] "2020_4_20.7083_56.6233" "2020_4_20.68_56.625" "2020_4_20.5983_56.64"
#> [7] "2020_4_20.2583_56.54" "2020_4_19.8717_56.1317" "2020_4_20.5433_57.0467"
#> [10] "2020_4_19.9117_56.17" "2020_4_20.08_56.3833" "2020_4_19.6617_56.2583"
#> [13] "2020_4_20.59_57.1717" "2020_4_20.6733_57.205" "2020_4_21.1217_57.5067"
#> [16] "2020_4_21.4283_57.4983" "2020_4_21.3433_57.41" "2020_4_21.1033_57.255"
#> [19] "2020_4_16.6483_54.8733" "2020_4_15.5817_54.3833" "2020_4_15.7833_54.38"
#> [22] "2020_4_16.005_54.4233" "2020_4_15.9917_54.405" "2020_4_16.4183_54.6567"
#> [25] "2020_4_16.6367_54.6933" "2020_4_16.8417_54.7517" "2020_4_16.8833_54.7417"
#> [28] "2020_4_17.3667_54.9517" "2020_4_17.48_54.85" "2020_4_17.3067_55.3167"
#> [31] "2020_4_17.9567_55.735" "2020_4_17.375_55.2567" "2020_4_17.3967_54.9967"
#> [34] "2020_4_17.31_55.235" "2020_4_19.1983_54.3817" "2020_4_18.1983_54.895"
#> [37] "2020_4_18.5733_54.8567" "2020_4_18.015_55.69" "2020_4_18.665_56.4567"
#> [40] "2020_4_18.6883_56.4733" "2020_4_18.96_55.1833" "2020_4_18.91_55.4417"
#> [43] "2020_4_18.9967_55.5117" "2020_4_18.6083_56.4017" "2020_4_19.0183_54.4033"
#> [46] "2020_4_18.76_54.7267" "2020_4_19.2833_54.41" "2020_4_18.535_55.485"
#> [49] "2020_4_18.6733_54.795" "2020_4_18.3567_56.1767" "2020_4_18.2233_55.5283"
#> [52] "2020_4_18.5167_54.9933" "2020_4_19.2283_54.395" "2020_4_19.0567_54.4167"
#> [55] "2020_4_19.3117_54.45" "2020_4_19.0133_54.3967" "2020_4_19.0367_54.4167"
#> [58] "2020_4_18.5033_54.8333" "2020_4_18.6067_54.915" "2020_4_18.1817_54.8983"
#> [61] "2020_4_18.93_54.525" "2020_4_18.9267_54.51" "2020_4_19.075_54.4383"
#> [64] "2020_4_19.04_54.4217" "2020_4_19.325_54.575" "2020_4_19.1717_54.6"
#> [67] "2020_4_19.1117_54.4333" "2020_4_16.9165_57.3647" "2020_4_17.0241_57.4144"
#> [70] "2020_4_17.0912_57.4622" "2020_4_17.0977_57.6175" "2020_4_18.1196_57.7804"
#> [73] "2020_4_17.5616_57.4828" "2020_4_19.1682_57.6135" "2020_4_19.3769_57.6885"
#> [76] "2020_4_19.4807_57.7685" "2020_4_19.4891_58.0354" "2020_4_19.439_58.0746"
#> [79] "2020_4_19.5044_57.876" "2020_4_19.2267_57.3361" "2020_4_18.9076_57.089"
#> [82] "2020_4_18.8636_57.1753" "2020_4_19.0668_57.2496" "2020_4_19.0591_57.3072"
#> [85] "2020_4_19.1085_57.319" "2020_4_17.9509_56.9956" "2020_4_17.9167_57.0532"
#> [88] "2020_4_17.4146_57.2805" "2020_4_17.184_56.9584" "2020_4_16.9939_56.7256"
#> [91] "2020_4_16.85_56.5655" "2020_4_13.6016_55.2125" "2020_4_13.925_55.286"
#> [94] "2020_4_13.9518_55.26" "2020_4_14.4799_55.4589" "2020_4_14.5095_55.6877"
#> [97] "2020_4_14.3632_55.6986" "2020_4_14.3795_55.7037" "2020_4_15.5667_55.883"
#> [100] "2020_4_15.5703_55.8469"
#dat %>% group_by(year) %>% summarise(n = n()) %>% as.data.frame()
ids <- dat$id_oxy[!dat$id_oxy %in% c(big_dat_oxy$id_oxy)]
dat %>% filter(id_oxy %in% ids)
#> filter: removed 9,261 rows (99%), 112 rows remaining
#> # A tibble: 112 × 17
#> year lat lon quarter Country Month haul.id IDx ices_…¹ sub_div lengt…²
#> <int> <dbl> <dbl> <int> <chr> <int> <chr> <chr> <chr> <chr> <dbl>
#> 1 1993 57.4 16.9 3 SWE 8 1993:3… 1993… 43G6 27 0
#> 2 1993 57.5 17.1 3 SWE 8 1993:3… 1993… 43G7 27 0
#> 3 1993 57.5 17.6 3 SWE 8 1993:3… 1993… 43G7 27 0
#> 4 1993 57.1 17.9 3 SWE 8 1993:3… 1993… 43G7 27 0
#> 5 1993 57.3 17.9 3 SWE 8 1993:3… 1993… 43G7 27 0
#> 6 1993 57.8 18.1 3 SWE 8 1993:3… 1993… 44G8 27 0
#> 7 1993 57.8 18.3 3 SWE 8 1993:3… 1993… 44G8 27 0
#> 8 1993 57.8 19.5 3 SWE 8 1993:3… 1993… 44G9 28 0
#> 9 1993 57.9 19.5 3 SWE 8 1993:3… 1993… 44G9 28 0
#> 10 1993 57.9 19.5 3 SWE 8 1993:3… 1993… 44G9 28 0
#> # … with 102 more rows, 6 more variables: id_haul_stomach <chr>, species <chr>,
#> # haul.id.size <chr>, substrate <chr>, depth <dbl>, id_oxy <chr>, and
#> # abbreviated variable names ¹ices_rect, ²length_cm
# Select only the columns we want to merge
big_dat_sub_oxy <- big_dat_oxy %>% dplyr::select(id_oxy, oxy)
# Remove duplicate ID (one oxy value per id)
big_dat_sub_oxy %>% group_by(id_oxy) %>% mutate(n = n()) %>% arrange(desc(n))
#> group_by: one grouping variable (id_oxy)
#> mutate (grouped): new variable 'n' (integer) with 4 unique values and 0% NA
#> # A tibble: 9,261 × 3
#> # Groups: id_oxy [9,198]
#> id_oxy oxy n
#> <chr> <dbl> <int>
#> 1 1999_1_15.3667_54.5667 7.05 4
#> 2 1999_1_15.3667_54.5667 7.05 4
#> 3 1999_1_15.3667_54.5667 7.05 4
#> 4 1999_1_15.3667_54.5667 7.05 4
#> 5 1994_1_18.7833_54.7 8.62 3
#> 6 1994_1_18.7833_54.7 8.62 3
#> 7 1994_1_18.7833_54.7 8.62 3
#> 8 2003_1_20.3333_56.6333 4.30 3
#> 9 2003_1_20.3333_56.6333 4.30 3
#> 10 2003_1_20.3333_56.6333 4.30 3
#> # … with 9,251 more rows
big_dat_sub_oxy2 <- big_dat_sub_oxy %>% distinct(id_oxy, .keep_all = TRUE)
#> distinct: removed 63 rows (1%), 9,198 rows remainingTemperature
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1664183191233.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1664183191233.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float bottomT[longitude,latitude,time]
#> standard_name: sea_water_potential_temperature_at_sea_floor
#> units: degrees_C
#> long_name: Sea floor potential temperature
#> missing_value: NaN
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:336
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15721.5
#> valid_max: 25917.5
#> latitude Size:523
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 48.49169921875
#> valid_max: 65.8914184570312
#> longitude Size:383
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 9.01375484466553
#> valid_max: 30.2357654571533
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_PHY_003_011
#> title: CMEMS V4 Reanalysis: NEMO model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20201201_20201201
#> file_quality_index: 1
#> creation_date: 2021-11-09 UTC
#> bullentin_date: 20201201
#> start_date: 2020-12-01 UTC
#> stop_date: 2020-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 48.49170 48.52503 48.55836 48.59170 48.62503 48.65836
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#> [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#> [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#> [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#> [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#> [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#> [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#> [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#> [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#> [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#> [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5
#> [325] 25582.5 25612.5 25642.5 25673.0 25703.5 25734.0 25764.5 25795.5 25826.0
#> [334] 25856.5 25887.0 25917.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 336
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get temperature
dname <- "bottomT"
temp_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(temp_array)
#> [1] 383 523 336
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
temp_array[temp_array == fillvalue$value] <- NA
# Next, we need to work with the months that correspond to the quarters that we use.
# loop through each time step, and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [26] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [51] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [76] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [101] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [126] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [151] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [176] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [201] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [226] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [251] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [276] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [301] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [326] 2 3 4 5 6 7 8 9 10 11 12
index_keep_q1 <- which(months < 4)
index_keep_q4 <- which(months > 9)
temp_q1 <- temp_array[, , index_keep_q1]
temp_q4 <- temp_array[, , index_keep_q4]
months_keep_q1 <- months[index_keep_q1]
months_keep_q4 <- months[index_keep_q4]
years_keep_q1 <- years[index_keep_q1]
years_keep_q4 <- years[index_keep_q4]
# Now we have an array with data for that quarter
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq_q1 <- seq(1, dim(temp_q1)[3], by = 3)
loop_seq_q4 <- seq(1, dim(temp_q4)[3], by = 3)
# Create objects that will hold data
dlist_q1 <- list()
dlist_q4 <- list()
temp_1 <- c()
temp_2 <- c()
temp_3 <- c()
temp_ave_q1 <- c()
temp_10 <- c()
temp_11 <- c()
temp_12 <- c()
temp_ave_q4 <- c()
# Now average by quarter. The vector loop_seq_q1 is 1, 4, 7 etc. So first i is 1, 2, 3,
# which is the index we want.
for(i in loop_seq_q1) {
temp_1 <- temp_q1[, , (i)]
temp_2 <- temp_q1[, , (i + 1)]
temp_3 <- temp_q1[, , (i + 2)]
temp_10 <- temp_q4[, , (i)]
temp_11 <- temp_q4[, , (i + 1)]
temp_12 <- temp_q4[, , (i + 2)]
temp_ave_q1 <- (temp_1 + temp_2 + temp_3) / 3
temp_ave_q4 <- (temp_10 + temp_11 + temp_12) / 3
list_pos_q1 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
list_pos_q4 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist_q1[[list_pos_q1]] <- temp_ave_q1
dlist_q4[[list_pos_q4]] <- temp_ave_q4
}
# Now name the lists with the year:
names(dlist_q1) <- unique(years_keep_q1)
names(dlist_q4) <- unique(years_keep_q4)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have temperature for
d_sub_temp_q1 <- dat %>% filter(quarter == 1) %>% filter(year %in% names(dlist_q1)) %>% droplevels()
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
#> filter: no rows removed
d_sub_temp_q4 <- dat %>% filter(quarter == 4) %>% filter(year %in% names(dlist_q4)) %>% droplevels()
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
#> filter: no rows removed
# Create data holding object
temp_data_list_q1 <- list()
temp_data_list_q4 <- list()
# ... And for the temperature raster
raster_list_q1 <- list()
raster_list_q4 <- list()
# Create factor year for indexing the list in the loop
d_sub_temp_q1$year_f <- as.factor(d_sub_temp_q1$year)
d_sub_temp_q4$year_f <- as.factor(d_sub_temp_q4$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_temp_q1$year_f)) {
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a year
temp_slice_q1 <- dlist_q1[[i]]
temp_slice_q4 <- dlist_q4[[i]]
# Create raster for that year (i)
r_q1 <- raster(t(temp_slice_q1), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
r_q4 <- raster(t(temp_slice_q4), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r_q1 <- flip(r_q1, direction = 'y')
r_q4 <- flip(r_q4, direction = 'y')
plot(r_q1, main = paste(i, "Q1"))
plot(r_q4, main = paste(i, "Q4"))
# Filter the same year (i) in the cpue data and select only coordinates
d_slice_q1 <- d_sub_temp_q1 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
d_slice_q4 <- d_sub_temp_q4 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp_q1 <- SpatialPoints(d_slice_q1)
data_sp_q4 <- SpatialPoints(d_slice_q4)
# Extract raster value (temperature)
rasValue_q1 <- raster::extract(r_q1, data_sp_q1)
rasValue_q4 <- raster::extract(r_q4, data_sp_q4)
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df_q1 <- as.data.frame(data_sp_q1)
df_q4 <- as.data.frame(data_sp_q4)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice_q1$temp <- rasValue_q1
d_slice_q4$temp <- rasValue_q4
# Add in which year
d_slice_q1$year <- i
d_slice_q4$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index_q1 <- as.numeric(d_slice_q1$year)[1] - 1992
index_q4 <- as.numeric(d_slice_q4$year)[1] - 1992
# Add each years' data in the list
temp_data_list_q1[[index_q1]] <- d_slice_q1
temp_data_list_q4[[index_q4]] <- d_slice_q4
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map_q1 <- rasterToPoints(r_q1)
map_q4 <- rasterToPoints(r_q4)
# Make the points a dataframe for ggplot
df_rast_q1 <- data.frame(map_q1)
df_rast_q4 <- data.frame(map_q4)
# Rename y-variable and add year
df_rast_q1 <- df_rast_q1 %>% rename("temp" = "layer") %>% mutate(year = i)
df_rast_q4 <- df_rast_q4 %>% rename("temp" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list_q1[[index_q1]] <- df_rast_q1
raster_list_q4[[index_q4]] <- df_rast_q4
# Make appropriate column headings
colnames(df_rast_q1) <- c("Longitude", "Latitude", "temp")
colnames(df_rast_q4) <- c("Longitude", "Latitude", "temp")
# Make a map for q1
ggplot(data = df_rast_q1, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = temp)) +
geom_point(data = d_slice_q1, aes(x = lon, y = lat, fill = temp),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-2, 17)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-2, 17)) +
NULL
ggsave(paste("figures/supp/cpue_temp_rasters/", i,"q1.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
# Make a map for q4
ggplot(data = df_rast_q4, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = temp)) +
geom_point(data = d_slice_q4, aes(x = lon, y = lat, fill = temp),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(-2, 17)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(-2, 17)) +
NULL
ggsave(paste("figures/supp/cpue_temp_rasters/", i,"q4.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 5,494 rows (98%), 101 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,420 rows (97%), 175 rows remaining
#> filter: removed 3,604 rows (98%), 62 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,441 rows (97%), 154 rows remaining
#> filter: removed 3,613 rows (99%), 53 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,412 rows (97%), 183 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,381 rows (96%), 214 rows remaining
#> filter: removed 3,591 rows (98%), 75 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,367 rows (96%), 228 rows remaining
#> filter: removed 3,598 rows (98%), 68 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,405 rows (97%), 190 rows remaining
#> filter: removed 3,572 rows (97%), 94 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,485 rows (98%), 110 rows remaining
#> filter: removed 3,577 rows (98%), 89 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,550 rows (97%), 116 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,450 rows (97%), 145 rows remaining
#> filter: removed 3,549 rows (97%), 117 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,434 rows (97%), 161 rows remaining
#> filter: removed 3,538 rows (97%), 128 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,388 rows (96%), 207 rows remaining
#> filter: removed 3,557 rows (97%), 109 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,377 rows (96%), 218 rows remaining
#> filter: removed 3,515 rows (96%), 151 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,407 rows (97%), 188 rows remaining
#> filter: removed 3,516 rows (96%), 150 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,360 rows (96%), 235 rows remaining
#> filter: removed 3,497 rows (95%), 169 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,373 rows (96%), 222 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,336 rows (95%), 259 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,355 rows (96%), 240 rows remaining
#> filter: removed 3,504 rows (96%), 162 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,352 rows (96%), 243 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,371 rows (96%), 224 rows remaining
#> filter: removed 3,533 rows (96%), 133 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,327 rows (95%), 268 rows remaining
#> filter: removed 3,518 rows (96%), 148 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,358 rows (96%), 237 rows remaining
#> filter: removed 3,500 rows (95%), 166 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,363 rows (96%), 232 rows remaining
#> filter: removed 3,453 rows (94%), 213 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,350 rows (96%), 245 rows remaining
#> filter: removed 3,441 rows (94%), 225 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,339 rows (95%), 256 rows remaining
#> filter: removed 3,456 rows (94%), 210 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,477 rows (98%), 118 rows remaining
#> filter: removed 3,574 rows (97%), 92 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,469 rows (98%), 126 rows remaining
#> filter: removed 3,564 rows (97%), 102 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_temp_q1 <- dplyr::bind_rows(temp_data_list_q1)
big_dat_temp_q4 <- dplyr::bind_rows(temp_data_list_q4)
big_dat_temp <- bind_rows(mutate(big_dat_temp_q1, quarter = 1),
mutate(big_dat_temp_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
big_raster_dat_temp_q1 <- dplyr::bind_rows(raster_list_q1)
big_raster_dat_temp_q4 <- dplyr::bind_rows(raster_list_q4)
big_raster_dat_temp <- bind_rows(mutate(big_raster_dat_temp_q1, quarter = 1),
mutate(big_raster_dat_temp_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
# Plot data, looks like there's big inter-annual variation but a positive trend
big_raster_dat_temp %>%
group_by(quarter, year) %>%
drop_na(temp) %>%
summarise(mean_temp = mean(temp)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(aes(year_num, mean_temp)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter) +
NULL
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> summarise: now 56 rows and 3 columns, one group variable remaining (quarter)
#> mutate (grouped): new variable 'year_num' (double) with 28 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new temperature column in the original data:
str(d_sub_temp_q1)
#> tibble [5,595 × 18] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:5595] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:5595] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:5595] 13 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:5595] 1 1 1 1 1 1 1 1 1 1 ...
#> $ Country : chr [1:5595] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:5595] 2 2 2 2 2 2 2 2 2 2 ...
#> $ haul.id : chr [1:5595] "1993:1:GFR:SOL:H20:21:1" "1993:1:GFR:SOL:H20:22:32" "1993:1:GFR:SOL:H20:23:31" "1993:1:GFR:SOL:H20:24:30" ...
#> $ IDx : chr [1:5595] "1993.1.GFR.06S1.H20.21.1" "1993.1.GFR.06S1.H20.22.32" "1993.1.GFR.06S1.H20.23.31" "1993.1.GFR.06S1.H20.24.30" ...
#> $ ices_rect : chr [1:5595] "38G3" "38G4" "38G4" "37G3" ...
#> $ sub_div : chr [1:5595] "24" "24" "24" "24" ...
#> $ length_cm : num [1:5595] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:5595] NA NA NA NA ...
#> $ species : chr [1:5595] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:5595] "1993:1:GFR:SOL:H20:21:1.0" "1993:1:GFR:SOL:H20:22:32.0" "1993:1:GFR:SOL:H20:23:31.0" "1993:1:GFR:SOL:H20:24:30.0" ...
#> $ substrate : chr [1:5595] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:5595] 9 7 8 6 13 15 11 15 10 10 ...
#> $ id_oxy : chr [1:5595] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167" "1993_1_13.9833_54.4833" ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(d_sub_temp_q4)
#> tibble [3,666 × 18] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:3666] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:3666] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:3666] 13.1 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:3666] 4 4 4 4 4 4 4 4 4 4 ...
#> $ Country : chr [1:3666] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:3666] 12 11 11 11 12 12 11 11 11 11 ...
#> $ haul.id : chr [1:3666] "1993:4:GFR:SOL:H20:21:65" "1993:4:GFR:SOL:H20:22:43" "1993:4:GFR:SOL:H20:23:44" "1993:4:GFR:SOL:H20:24:46" ...
#> $ IDx : chr [1:3666] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" "1993.4.GFR.06S1.H20.23.44" "1993.4.GFR.06S1.H20.24.46" ...
#> $ ices_rect : chr [1:3666] "38G3" "38G4" "38G4" "37G4" ...
#> $ sub_div : chr [1:3666] "24" "24" "24" "24" ...
#> $ length_cm : num [1:3666] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:3666] NA NA NA NA ...
#> $ species : chr [1:3666] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:3666] "1993:4:GFR:SOL:H20:21:65.0" "1993:4:GFR:SOL:H20:22:43.0" "1993:4:GFR:SOL:H20:23:44.0" "1993:4:GFR:SOL:H20:24:46.0" ...
#> $ substrate : chr [1:3666] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:3666] 8 7 8 7 16 17 11 18 10 10 ...
#> $ id_oxy : chr [1:3666] "1993_4_13.05_54.6667" "1993_4_14.3167_54.5167" "1993_4_14.2_54.5167" "1993_4_14.0333_54.4833" ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_temp)
#> tibble [9,261 × 5] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:9261] 13 14.3 14.2 14 13.1 ...
#> $ lat : num [1:9261] 54.7 54.5 54.5 54.5 54.7 ...
#> $ temp : num [1:9261] 3.36 3.2 3.15 3.14 3.33 ...
#> $ year : chr [1:9261] "1993" "1993" "1993" "1993" ...
#> $ quarter: num [1:9261] 1 1 1 1 1 1 1 1 1 1 ...
# Create an ID for matching the temperature data with the cpue data
dat$id_temp <- paste(dat$year, dat$quarter, dat$lon, dat$lat, sep = "_")
big_dat_temp$id_temp <- paste(big_dat_temp$year, big_dat_temp$quarter, big_dat_temp$lon, big_dat_temp$lat, sep = "_")
# Which id's are not in the cpue data (dat)? (It's because I don't have those years, not about the location)
ids <- dat$id_temp[!dat$id_temp %in% c(big_dat_temp$id_temp)]
unique(ids)
#> [1] "1993_3_16.8935_57.3822" "1993_3_17.0851_57.4723" "1993_3_17.5683_57.4833"
#> [4] "1993_3_17.9133_57.0566" "1993_3_17.8833_57.3383" "1993_3_18.1158_57.758"
#> [7] "1993_3_18.3158_57.7593" "1993_3_19.5258_57.8453" "1993_3_19.465_57.855"
#> [10] "1993_3_19.4568_57.9165" "1993_3_19.6068_57.9796" "1993_3_19.2133_57.3967"
#> [13] "1993_3_18.8516_57.175" "1993_3_18.8395_57.0797" "1993_3_18.8798_57.0675"
#> [16] "1993_3_18.4721_56.2501" "1993_3_17.7942_56.0955" "1993_3_17.6995_55.8498"
#> [19] "1994_3_17.9067_57.0966" "1994_3_17.9883_57.0383" "1994_3_17.625_55.4333"
#> [22] "1994_3_17.1167_55.925" "1994_3_16.5283_55.6783" "1994_3_15.9783_55.4783"
#> [25] "1994_3_16.0417_55.315" "1994_3_14.73_55.4583" "1994_3_14.4983_55.46"
#> [28] "1994_3_14.4967_55.665" "1994_3_15.375_55.9466" "1995_3_13.225_55.21"
#> [31] "1995_3_13.585_55.21" "1995_3_13.26_54.965" "1995_3_13.9067_55.01"
#> [34] "1995_3_15.4167_55.9767" "1995_3_15.2933_55.6" "1995_3_14.5017_55.6667"
#> [37] "1995_3_14.6167_55.5933" "1995_3_14.495_55.46" "1995_3_14.6833_55.4567"
#> [40] "1995_3_16.3217_55.525" "1995_3_18.4017_55.5533" "1995_3_18.9083_56.0017"
#> [43] "1995_3_18.4633_56.24" "1995_3_17.775_56.0817" "1995_3_18.875_57.0617"
#> [46] "1995_3_18.8617_57.175" "1995_3_17.95_56.9933" "1995_3_17.915_57.065"
#> [49] "1995_3_17.6983_55.8466" "1995_3_17.11_55.9283" "1996_2_18.8_54.6833"
#> [52] "1996_2_18.75_54.7333" "1996_2_18.6833_54.7833" "1996_2_18.7_54.7333"
#> [55] "1996_2_18.6_54.8" "1996_2_18.6333_54.8333" "1996_2_18.6333_54.8833"
#> [58] "1996_2_18.65_54.8833" "1996_2_18.6833_54.8833" "1996_2_18.7167_54.8667"
#> [61] "1996_2_18.7333_54.9" "1996_2_17.4_55.4667" "1996_2_17.4333_55.4333"
#> [64] "1996_2_17.6167_55.4667" "1996_2_17.5833_55.4" "1996_2_17.45_55.35"
#> [67] "1996_2_17.3667_55.2333" "1996_2_18.85_54.9333" "1997_2_20.5167_56.5667"
#> [70] "1997_2_19.6833_56.15" "1997_2_19.2667_56.0667" "1997_2_19.5667_56.3"
#> [73] "1997_2_19.8_56.3833" "1997_2_19.9667_56.4833" "1997_2_20.2333_56.5667"
#> [76] "1997_2_20.5333_56.65" "1997_2_20.8_56.7167" "1997_2_20.8167_56.9167"
#> [79] "1997_2_20.7833_56.9833" "1997_2_20.35_56.45" "1997_2_20.2167_56.4667"
#> [82] "1997_2_20.1333_56.3167" "1997_2_19.8_56.2333" "1998_2_21.4_57.7167"
#> [85] "1998_2_20.6667_57.1333" "1998_2_20.6_57.0833" "1998_2_20.25_56.9333"
#> [88] "1998_2_20.3_56.65" "1998_2_20.2667_56.5667" "1998_2_20.3333_56.4667"
#> [91] "1998_2_19.8_56.3833" "1998_2_21.3667_57.7833" "1998_2_21.2667_57.8333"
#> [94] "1998_2_21.1167_57.8333" "1998_2_21.1667_57.5667" "1998_2_21.15_57.4833"
#> [97] "1998_2_21.2667_57.3667" "1998_2_21.1167_57.25" "1998_2_20.7167_57.25"
#> [100] "1999_2_21.1_57.05" "1999_2_21.1167_57.0333" "1999_2_20.7167_57.1833"
#> [103] "1999_2_20.7333_57.2167" "1999_2_20.7333_57.2667" "1999_2_20.55_57.1667"
#> [106] "1999_2_20.65_57.05" "1999_2_20.7167_57.3667" "1999_2_20.8667_57.35"
#> [109] "1999_2_21.1167_57.25" "1999_2_21.15_57.2333" "1999_2_21.2667_57.35"
length(unique(ids))
#> [1] 111
length(unique(dat$id_temp))
#> [1] 9309
# Select only the columns we want to merge
big_dat_sub_temp <- big_dat_temp %>% dplyr::select(id_temp, temp)
# Remove duplicate ID (one temp value per id)
big_dat_sub_temp2 <- big_dat_sub_temp %>% distinct(id_temp, .keep_all = TRUE)
#> distinct: removed 63 rows (1%), 9,198 rows remainingBottom salinity
# https://data.marine.copernicus.eu/product/BALTICSEA_REANALYSIS_PHY_003_011/download?dataset=dataset-reanalysis-nemo-monthlymeans
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1668587452211.nc")
print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1668587452211.nc (NC_FORMAT_CLASSIC):
#>
#> 1 variables (excluding dimension variables):
#> float sob[longitude,latitude,time]
#> long_name: Sea water salinity at sea floor
#> missing_value: NaN
#> standard_name: sea_water_salinity
#> units: 0.001
#> _FillValue: NaN
#> _ChunkSizes: 1
#> _ChunkSizes: 523
#> _ChunkSizes: 383
#>
#> 3 dimensions:
#> time Size:335
#> axis: T
#> long_name: Validity time
#> standard_name: time
#> units: days since 1950-01-01 00:00:00
#> calendar: gregorian
#> _ChunkSizes: 512
#> _CoordinateAxisType: Time
#> valid_min: 15751
#> valid_max: 25917.5
#> latitude Size:187
#> axis: Y
#> standard_name: latitude
#> long_name: latitude
#> units: degrees_north
#> _CoordinateAxisType: Lat
#> valid_min: 53.1249580383301
#> valid_max: 59.3248596191406
#> longitude Size:199
#> standard_name: longitude
#> long_name: longitude
#> units: degrees_east
#> axis: X
#> _CoordinateAxisType: Lon
#> valid_min: 11.1248445510864
#> valid_max: 22.12473487854
#>
#> 24 global attributes:
#> references: http://www.smhi.se
#> institution: Swedish Meterological and Hydrological Institute
#> history: See source and creation_date attributees
#> Conventions: CF-1.5
#> contact: servicedesk_cmems@mercator-ocean.eu
#> comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#> bullentin_type: reanalysis
#> cmems_product_id: BALTICSEA_REANALYSIS_PHY_003_011
#> title: CMEMS V4 Reanalysis: NEMO model 3D fields (monthly means)
#> FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#> FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#> FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#> FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#> shallowest_depth: 1.50136542320251
#> deepest_depth: 711.059204101562
#> source: SMHI reanalysis run NORDIC-NS2_1d_20201201_20201201
#> file_quality_index: 1
#> creation_date: 2021-11-09 UTC
#> bullentin_date: 20201201
#> start_date: 2020-12-01 UTC
#> stop_date: 2020-12-01 UTC
#> start_time: 00:00 UTC
#> stop_time: 00:00 UTC
#> _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 11.12484 11.18040 11.23596 11.29151 11.34706 11.40262
lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 53.12496 53.15829 53.19162 53.22496 53.25829 53.29162
# Get time
time <- ncvar_get(ncin,"time")
time
#> [1] 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0 15994.5
#> [10] 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0 16267.5
#> [19] 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5 16541.0
#> [28] 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5 16816.5
#> [37] 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0 17090.5
#> [46] 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0 17363.5
#> [55] 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5 17637.0
#> [64] 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5 17912.5
#> [73] 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0 18185.5
#> [82] 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0 18459.5
#> [91] 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5 18733.0
#> [100] 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5 19008.5
#> [109] 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0 19281.5
#> [118] 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0 19554.5
#> [127] 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5 19829.0
#> [136] 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5 20104.5
#> [145] 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0 20377.5
#> [154] 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0 20650.5
#> [163] 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5 20924.0
#> [172] 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5 21199.5
#> [181] 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0 21473.5
#> [190] 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0 21746.5
#> [199] 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5 22020.0
#> [208] 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5 22295.5
#> [217] 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0 22568.5
#> [226] 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0 22842.5
#> [235] 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5 23116.0
#> [244] 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5 23391.5
#> [253] 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0 23664.5
#> [262] 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0 23937.5
#> [271] 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5 24212.0
#> [280] 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5 24487.5
#> [289] 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0 24760.5
#> [298] 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0 25033.5
#> [307] 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5 25307.0
#> [316] 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5 25582.5
#> [325] 25612.5 25642.5 25673.0 25703.5 25734.0 25764.5 25795.5 25826.0 25856.5
#> [334] 25887.0 25917.5
tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 335
tunits
#> $hasatt
#> [1] TRUE
#>
#> $value
#> [1] "days since 1950-01-01 00:00:00"
# Get Salinity
dname <- "sob"
sal_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(sal_array)
#> [1] 199 187 335
# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
sal_array[sal_array == fillvalue$value] <- NA
# Next, we need to work with the months that correspond to the quarters that we use.
# loop through each time step, and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#> [1] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [26] 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
#> [51] 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4
#> [76] 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5
#> [101] 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
#> [126] 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7
#> [151] 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8
#> [176] 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
#> [201] 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
#> [226] 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11
#> [251] 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
#> [276] 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1
#> [301] 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2
#> [326] 3 4 5 6 7 8 9 10 11 12
index_keep_q1 <- which(months < 4)
index_keep_q4 <- which(months > 9)
sal_q1 <- sal_array[, , index_keep_q1]
sal_q4 <- sal_array[, , index_keep_q4]
months_keep_q1 <- months[index_keep_q1]
months_keep_q4 <- months[index_keep_q4]
years_keep_q1 <- years[index_keep_q1]
years_keep_q4 <- years[index_keep_q4]
# Now we have an array with data for that quarter
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq_q1 <- seq(1, dim(sal_q1)[3], by = 3)
loop_seq_q4 <- seq(1, dim(sal_q4)[3], by = 3)
# Create objects that will hold data
dlist_q1 <- list()
dlist_q4 <- list()
sal_1 <- c()
sal_2 <- c()
sal_3 <- c()
sal_ave_q1 <- c()
sal_10 <- c()
sal_11 <- c()
sal_12 <- c()
sal_ave_q4 <- c()
# Now average by quarter. The vector loop_seq_q1 is 1, 4, 7 etc. So first i is 1, 2, 3,
# which is the index we want.
dim(sal_q1)
#> [1] 199 187 83
dim(sal_q4)
#> [1] 199 187 84
# Hmm, we didn't get the first month in the salinity series... repeat month 2 and fill in so the dimensions are correct
sal_q1 <- sal_q1[,,c(1, 1:83)]
dim(sal_q1)
#> [1] 199 187 84
for(i in loop_seq_q1) {
sal_1 <- sal_q1[, , (i)]
sal_2 <- sal_q1[, , (i + 1)]
sal_3 <- sal_q1[, , (i + 2)]
sal_10 <- sal_q4[, , (i)]
sal_11 <- sal_q4[, , (i + 1)]
sal_12 <- sal_q4[, , (i + 2)]
sal_ave_q1 <- (sal_1 + sal_2 + sal_3) / 3
sal_ave_q4 <- (sal_10 + sal_11 + sal_12) / 3
list_pos_q1 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
list_pos_q4 <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
dlist_q1[[list_pos_q1]] <- sal_ave_q1
dlist_q4[[list_pos_q4]] <- sal_ave_q4
}
# Now name the lists with the year:
names(dlist_q1) <- unique(years_keep_q1)
names(dlist_q4) <- unique(years_keep_q4)
# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script
# Filter years in the cpue data frame to only have the years I have salinity for
d_sub_sal_q1 <- dat %>% filter(quarter == 1) %>% filter(year %in% names(dlist_q1)) %>% droplevels()
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
#> filter: no rows removed
d_sub_sal_q4 <- dat %>% filter(quarter == 4) %>% filter(year %in% names(dlist_q4)) %>% droplevels()
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
#> filter: no rows removed
# Create data holding object
sal_data_list_q1 <- list()
sal_data_list_q4 <- list()
# ... And for the salinity raster
raster_list_q1 <- list()
raster_list_q4 <- list()
# Create factor year for indexing the list in the loop
d_sub_sal_q1$year_f <- as.factor(d_sub_sal_q1$year)
d_sub_sal_q4$year_f <- as.factor(d_sub_sal_q4$year)
# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_sal_q1$year_f)) {
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a year
sal_slice_q1 <- dlist_q1[[i]]
sal_slice_q4 <- dlist_q4[[i]]
# Create raster for that year (i)
r_q1 <- raster(t(sal_slice_q1), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
r_q4 <- raster(t(sal_slice_q4), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r_q1 <- flip(r_q1, direction = 'y')
r_q4 <- flip(r_q4, direction = 'y')
plot(r_q1, main = paste(i, "Q1"))
plot(r_q4, main = paste(i, "Q4"))
# Filter the same year (i) in the cpue data and select only coordinates
d_slice_q1 <- d_sub_sal_q1 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
d_slice_q4 <- d_sub_sal_q4 %>% filter(year_f == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp_q1 <- SpatialPoints(d_slice_q1)
data_sp_q4 <- SpatialPoints(d_slice_q4)
# Extract raster value (salinity)
rasValue_q1 <- raster::extract(r_q1, data_sp_q1)
rasValue_q4 <- raster::extract(r_q4, data_sp_q4)
# Now we want to plot the results of the raster extractions by plotting the cpue
# data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for pl)
df_q1 <- as.data.frame(data_sp_q1)
df_q4 <- as.data.frame(data_sp_q4)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice_q1$sal <- rasValue_q1
d_slice_q4$sal <- rasValue_q4
# Add in which year
d_slice_q1$year <- i
d_slice_q4$year <- i
# Create a index for the data last where we store all years (because our loop index
# i is not continuous, we can't use it directly)
index_q1 <- as.numeric(d_slice_q1$year)[1] - 1992
index_q4 <- as.numeric(d_slice_q4$year)[1] - 1992
# Add each years' data in the list
sal_data_list_q1[[index_q1]] <- d_slice_q1
sal_data_list_q4[[index_q4]] <- d_slice_q4
# Save to check each year is ok! First convert the raster to points for plotting
# (so that we can use ggplot)
map_q1 <- rasterToPoints(r_q1)
map_q4 <- rasterToPoints(r_q4)
# Make the points a dataframe for ggplot
df_rast_q1 <- data.frame(map_q1)
df_rast_q4 <- data.frame(map_q4)
# Rename y-variable and add year
df_rast_q1 <- df_rast_q1 %>% rename("sal" = "layer") %>% mutate(year = i)
df_rast_q4 <- df_rast_q4 %>% rename("sal" = "layer") %>% mutate(year = i)
# Add each years' raster data frame in the list
raster_list_q1[[index_q1]] <- df_rast_q1
raster_list_q4[[index_q4]] <- df_rast_q4
# Make appropriate column headings
colnames(df_rast_q1) <- c("Longitude", "Latitude", "sal")
colnames(df_rast_q4) <- c("Longitude", "Latitude", "sal")
# Make a map for q1
ggplot(data = df_rast_q1, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = sal)) +
geom_point(data = d_slice_q1, aes(x = lon, y = lat, fill = sal),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 36)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 36)) +
NULL
ggsave(paste("figures/supp/cpue_sal_rasters/", i,"q1.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
# Make a map for q4
ggplot(data = df_rast_q4, aes(y = Latitude, x = Longitude)) +
geom_raster(aes(fill = sal)) +
geom_point(data = d_slice_q4, aes(x = lon, y = lat, fill = sal),
color = "black", size = 5, shape = 21) +
theme_bw() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
scale_colour_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 36)) +
scale_fill_gradientn(colours = rev(terrain.colors(10)),
limits = c(2, 36)) +
NULL
ggsave(paste("figures/supp/cpue_sal_rasters/", i,"q4.png", sep = ""),
width = 6.5, height = 6.5, dpi = 600)
}
#> filter: removed 5,494 rows (98%), 101 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,420 rows (97%), 175 rows remaining
#> filter: removed 3,604 rows (98%), 62 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,441 rows (97%), 154 rows remaining
#> filter: removed 3,613 rows (99%), 53 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,412 rows (97%), 183 rows remaining
#> filter: removed 3,605 rows (98%), 61 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,381 rows (96%), 214 rows remaining
#> filter: removed 3,591 rows (98%), 75 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,367 rows (96%), 228 rows remaining
#> filter: removed 3,598 rows (98%), 68 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,405 rows (97%), 190 rows remaining
#> filter: removed 3,572 rows (97%), 94 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,485 rows (98%), 110 rows remaining
#> filter: removed 3,577 rows (98%), 89 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,550 rows (97%), 116 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,450 rows (97%), 145 rows remaining
#> filter: removed 3,549 rows (97%), 117 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,434 rows (97%), 161 rows remaining
#> filter: removed 3,538 rows (97%), 128 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,388 rows (96%), 207 rows remaining
#> filter: removed 3,557 rows (97%), 109 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,377 rows (96%), 218 rows remaining
#> filter: removed 3,515 rows (96%), 151 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,407 rows (97%), 188 rows remaining
#> filter: removed 3,516 rows (96%), 150 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,360 rows (96%), 235 rows remaining
#> filter: removed 3,497 rows (95%), 169 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,373 rows (96%), 222 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,336 rows (95%), 259 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,355 rows (96%), 240 rows remaining
#> filter: removed 3,504 rows (96%), 162 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,352 rows (96%), 243 rows remaining
#> filter: removed 3,486 rows (95%), 180 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,371 rows (96%), 224 rows remaining
#> filter: removed 3,533 rows (96%), 133 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,327 rows (95%), 268 rows remaining
#> filter: removed 3,518 rows (96%), 148 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,387 rows (96%), 208 rows remaining
#> filter: removed 3,490 rows (95%), 176 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,358 rows (96%), 237 rows remaining
#> filter: removed 3,500 rows (95%), 166 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,363 rows (96%), 232 rows remaining
#> filter: removed 3,453 rows (94%), 213 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,350 rows (96%), 245 rows remaining
#> filter: removed 3,441 rows (94%), 225 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,339 rows (95%), 256 rows remaining
#> filter: removed 3,456 rows (94%), 210 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,477 rows (98%), 118 rows remaining
#> filter: removed 3,574 rows (97%), 92 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 5,469 rows (98%), 126 rows remaining
#> filter: removed 3,564 rows (97%), 102 rows remaining
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> rename: renamed one variable (sal)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
# Now create a data frame from the list of all annual values
big_dat_sal_q1 <- dplyr::bind_rows(sal_data_list_q1)
big_dat_sal_q4 <- dplyr::bind_rows(sal_data_list_q4)
big_dat_sal <- bind_rows(mutate(big_dat_sal_q1, quarter = 1),
mutate(big_dat_sal_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
big_raster_dat_sal_q1 <- dplyr::bind_rows(raster_list_q1)
big_raster_dat_sal_q4 <- dplyr::bind_rows(raster_list_q4)
big_raster_dat_sal <- bind_rows(mutate(big_raster_dat_sal_q1, quarter = 1),
mutate(big_raster_dat_sal_q4, quarter = 4))
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
#> mutate: new variable 'quarter' (double) with one unique value and 0% NA
# Plot data, looks like there's big inter-annual variation but a positive trend
big_raster_dat_sal %>%
group_by(quarter, year) %>%
drop_na(sal) %>%
summarise(mean_sal = mean(sal)) %>%
mutate(year_num = as.numeric(year)) %>%
ggplot(aes(year_num, mean_sal)) +
geom_point(size = 2) +
stat_smooth(method = "lm") +
facet_wrap(~ quarter) +
NULL
#> group_by: 2 grouping variables (quarter, year)
#> drop_na (grouped): no rows removed
#> summarise: now 56 rows and 3 columns, one group variable remaining (quarter)
#> mutate (grouped): new variable 'year_num' (double) with 28 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'
# Now add in the new salinity column in the original data:
str(d_sub_sal_q1)
#> tibble [5,595 × 19] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:5595] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:5595] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:5595] 13 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:5595] 1 1 1 1 1 1 1 1 1 1 ...
#> $ Country : chr [1:5595] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:5595] 2 2 2 2 2 2 2 2 2 2 ...
#> $ haul.id : chr [1:5595] "1993:1:GFR:SOL:H20:21:1" "1993:1:GFR:SOL:H20:22:32" "1993:1:GFR:SOL:H20:23:31" "1993:1:GFR:SOL:H20:24:30" ...
#> $ IDx : chr [1:5595] "1993.1.GFR.06S1.H20.21.1" "1993.1.GFR.06S1.H20.22.32" "1993.1.GFR.06S1.H20.23.31" "1993.1.GFR.06S1.H20.24.30" ...
#> $ ices_rect : chr [1:5595] "38G3" "38G4" "38G4" "37G3" ...
#> $ sub_div : chr [1:5595] "24" "24" "24" "24" ...
#> $ length_cm : num [1:5595] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:5595] NA NA NA NA ...
#> $ species : chr [1:5595] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:5595] "1993:1:GFR:SOL:H20:21:1.0" "1993:1:GFR:SOL:H20:22:32.0" "1993:1:GFR:SOL:H20:23:31.0" "1993:1:GFR:SOL:H20:24:30.0" ...
#> $ substrate : chr [1:5595] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:5595] 9 7 8 6 13 15 11 15 10 10 ...
#> $ id_oxy : chr [1:5595] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167" "1993_1_13.9833_54.4833" ...
#> $ id_temp : chr [1:5595] "1993_1_13_54.6833" "1993_1_14.2667_54.5167" "1993_1_14.15_54.5167" "1993_1_13.9833_54.4833" ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(d_sub_sal_q4)
#> tibble [3,666 × 19] (S3: tbl_df/tbl/data.frame)
#> $ year : int [1:3666] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#> $ lat : num [1:3666] 54.7 54.5 54.5 54.5 54.7 ...
#> $ lon : num [1:3666] 13.1 14.3 14.2 14 13.1 ...
#> $ quarter : int [1:3666] 4 4 4 4 4 4 4 4 4 4 ...
#> $ Country : chr [1:3666] "GFR" "GFR" "GFR" "GFR" ...
#> $ Month : int [1:3666] 12 11 11 11 12 12 11 11 11 11 ...
#> $ haul.id : chr [1:3666] "1993:4:GFR:SOL:H20:21:65" "1993:4:GFR:SOL:H20:22:43" "1993:4:GFR:SOL:H20:23:44" "1993:4:GFR:SOL:H20:24:46" ...
#> $ IDx : chr [1:3666] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" "1993.4.GFR.06S1.H20.23.44" "1993.4.GFR.06S1.H20.24.46" ...
#> $ ices_rect : chr [1:3666] "38G3" "38G4" "38G4" "37G4" ...
#> $ sub_div : chr [1:3666] "24" "24" "24" "24" ...
#> $ length_cm : num [1:3666] 0 0 0 0 0 0 0 0 0 0 ...
#> $ id_haul_stomach: chr [1:3666] NA NA NA NA ...
#> $ species : chr [1:3666] "cod" "cod" "cod" "cod" ...
#> $ haul.id.size : chr [1:3666] "1993:4:GFR:SOL:H20:21:65.0" "1993:4:GFR:SOL:H20:22:43.0" "1993:4:GFR:SOL:H20:23:44.0" "1993:4:GFR:SOL:H20:24:46.0" ...
#> $ substrate : chr [1:3666] "sand" "sand" "sand" "sand" ...
#> $ depth : num [1:3666] 8 7 8 7 16 17 11 18 10 10 ...
#> $ id_oxy : chr [1:3666] "1993_4_13.05_54.6667" "1993_4_14.3167_54.5167" "1993_4_14.2_54.5167" "1993_4_14.0333_54.4833" ...
#> $ id_temp : chr [1:3666] "1993_4_13.05_54.6667" "1993_4_14.3167_54.5167" "1993_4_14.2_54.5167" "1993_4_14.0333_54.4833" ...
#> $ year_f : Factor w/ 28 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_sal)
#> tibble [9,261 × 5] (S3: tbl_df/tbl/data.frame)
#> $ lon : num [1:9261] 13 14.3 14.2 14 13.1 ...
#> $ lat : num [1:9261] 54.7 54.5 54.5 54.5 54.7 ...
#> $ sal : num [1:9261] 12.69 8.85 8.87 9.18 12.78 ...
#> $ year : chr [1:9261] "1993" "1993" "1993" "1993" ...
#> $ quarter: num [1:9261] 1 1 1 1 1 1 1 1 1 1 ...
# Create an ID for matching the salinity data with the cpue data
dat$id_sal <- paste(dat$year, dat$quarter, dat$lon, dat$lat, sep = "_")
big_dat_sal$id_sal <- paste(big_dat_sal$year, big_dat_sal$quarter, big_dat_sal$lon, big_dat_sal$lat, sep = "_")
# Which id's are not in the cpue data (dat)? (It's because I don't have those years, not about the location)
ids <- dat$id_sal[!dat$id_sal %in% c(big_dat_sal$id_sal)]
unique(ids)
#> [1] "1993_3_16.8935_57.3822" "1993_3_17.0851_57.4723" "1993_3_17.5683_57.4833"
#> [4] "1993_3_17.9133_57.0566" "1993_3_17.8833_57.3383" "1993_3_18.1158_57.758"
#> [7] "1993_3_18.3158_57.7593" "1993_3_19.5258_57.8453" "1993_3_19.465_57.855"
#> [10] "1993_3_19.4568_57.9165" "1993_3_19.6068_57.9796" "1993_3_19.2133_57.3967"
#> [13] "1993_3_18.8516_57.175" "1993_3_18.8395_57.0797" "1993_3_18.8798_57.0675"
#> [16] "1993_3_18.4721_56.2501" "1993_3_17.7942_56.0955" "1993_3_17.6995_55.8498"
#> [19] "1994_3_17.9067_57.0966" "1994_3_17.9883_57.0383" "1994_3_17.625_55.4333"
#> [22] "1994_3_17.1167_55.925" "1994_3_16.5283_55.6783" "1994_3_15.9783_55.4783"
#> [25] "1994_3_16.0417_55.315" "1994_3_14.73_55.4583" "1994_3_14.4983_55.46"
#> [28] "1994_3_14.4967_55.665" "1994_3_15.375_55.9466" "1995_3_13.225_55.21"
#> [31] "1995_3_13.585_55.21" "1995_3_13.26_54.965" "1995_3_13.9067_55.01"
#> [34] "1995_3_15.4167_55.9767" "1995_3_15.2933_55.6" "1995_3_14.5017_55.6667"
#> [37] "1995_3_14.6167_55.5933" "1995_3_14.495_55.46" "1995_3_14.6833_55.4567"
#> [40] "1995_3_16.3217_55.525" "1995_3_18.4017_55.5533" "1995_3_18.9083_56.0017"
#> [43] "1995_3_18.4633_56.24" "1995_3_17.775_56.0817" "1995_3_18.875_57.0617"
#> [46] "1995_3_18.8617_57.175" "1995_3_17.95_56.9933" "1995_3_17.915_57.065"
#> [49] "1995_3_17.6983_55.8466" "1995_3_17.11_55.9283" "1996_2_18.8_54.6833"
#> [52] "1996_2_18.75_54.7333" "1996_2_18.6833_54.7833" "1996_2_18.7_54.7333"
#> [55] "1996_2_18.6_54.8" "1996_2_18.6333_54.8333" "1996_2_18.6333_54.8833"
#> [58] "1996_2_18.65_54.8833" "1996_2_18.6833_54.8833" "1996_2_18.7167_54.8667"
#> [61] "1996_2_18.7333_54.9" "1996_2_17.4_55.4667" "1996_2_17.4333_55.4333"
#> [64] "1996_2_17.6167_55.4667" "1996_2_17.5833_55.4" "1996_2_17.45_55.35"
#> [67] "1996_2_17.3667_55.2333" "1996_2_18.85_54.9333" "1997_2_20.5167_56.5667"
#> [70] "1997_2_19.6833_56.15" "1997_2_19.2667_56.0667" "1997_2_19.5667_56.3"
#> [73] "1997_2_19.8_56.3833" "1997_2_19.9667_56.4833" "1997_2_20.2333_56.5667"
#> [76] "1997_2_20.5333_56.65" "1997_2_20.8_56.7167" "1997_2_20.8167_56.9167"
#> [79] "1997_2_20.7833_56.9833" "1997_2_20.35_56.45" "1997_2_20.2167_56.4667"
#> [82] "1997_2_20.1333_56.3167" "1997_2_19.8_56.2333" "1998_2_21.4_57.7167"
#> [85] "1998_2_20.6667_57.1333" "1998_2_20.6_57.0833" "1998_2_20.25_56.9333"
#> [88] "1998_2_20.3_56.65" "1998_2_20.2667_56.5667" "1998_2_20.3333_56.4667"
#> [91] "1998_2_19.8_56.3833" "1998_2_21.3667_57.7833" "1998_2_21.2667_57.8333"
#> [94] "1998_2_21.1167_57.8333" "1998_2_21.1667_57.5667" "1998_2_21.15_57.4833"
#> [97] "1998_2_21.2667_57.3667" "1998_2_21.1167_57.25" "1998_2_20.7167_57.25"
#> [100] "1999_2_21.1_57.05" "1999_2_21.1167_57.0333" "1999_2_20.7167_57.1833"
#> [103] "1999_2_20.7333_57.2167" "1999_2_20.7333_57.2667" "1999_2_20.55_57.1667"
#> [106] "1999_2_20.65_57.05" "1999_2_20.7167_57.3667" "1999_2_20.8667_57.35"
#> [109] "1999_2_21.1167_57.25" "1999_2_21.15_57.2333" "1999_2_21.2667_57.35"
length(unique(ids))
#> [1] 111
length(unique(dat$id_sal))
#> [1] 9309
# Select only the columns we want to merge
big_dat_sub_sal <- big_dat_sal %>% dplyr::select(id_sal, sal)
# Remove duplicate ID (one salinity value per id)
big_dat_sub_sal2 <- big_dat_sub_sal %>% distinct(id_sal, .keep_all = TRUE)
#> distinct: removed 63 rows (1%), 9,198 rows remainingenv_dat <- left_join(rename(big_dat_sub_oxy2, id_env = id_oxy),
rename(big_dat_sub_temp2, id_env = id_temp))
#> rename: renamed one variable (id_env)
#> rename: renamed one variable (id_env)
#> Joining, by = "id_env"
#> left_join: added one column (temp)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 9,198
#> > =======
#> > rows total 9,198
env_dat <- left_join(env_dat, rename(big_dat_sub_sal2, id_env = id_sal))
#> rename: renamed one variable (id_env)
#> Joining, by = "id_env"left_join: added one column (sal)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 9,198
#> > =======
#> > rows total 9,198
dat <- dat %>% dplyr::select(-id_oxy, id_sal) %>% rename(id_env = id_temp) %>% left_join(env_dat)
#> rename: renamed one variable (id_env)
#> Joining, by = "id_env"left_join: added 3 columns (oxy, temp, sal)
#> > rows only in x 112
#> > rows only in y ( 0)
#> > matched rows 9,261
#> > =======
#> > rows total 9,373
# Select only columns that are not already in the full_dat
dat <- dat %>% dplyr::select(haul.id, substrate, depth, temp, oxy, sal)
# Now join these data with the full_dat
dat_full <- left_join(dat_full, dat)
#> Joining, by = "haul.id"
#> left_join: added 5 columns (substrate, depth, temp, oxy, sal)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 1,759,609
#> > ===========
#> > rows total 1,759,609
# Temperature
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 1) %>%
ggplot(aes(y = lat, x = lon, color = temp)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 4) %>%
ggplot(aes(y = lat, x = lon, color = temp)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
# Oxygen
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 1) %>%
ggplot(aes(y = lat, x = lon, color = oxy)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 4) %>%
ggplot(aes(y = lat, x = lon, color = oxy)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
# Salinity
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 1) %>%
ggplot(aes(y = lat, x = lon, color = sal)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 3,778 rows (40%), 5,595 rows remaining
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
filter(quarter == 4) %>%
ggplot(aes(y = lat, x = lon, color = sal)) +
geom_point() +
geom_sf(data = world, inherit.aes = F, size = 0.2) +
coord_sf(xlim = c(xmin, xmax),
ylim = c(ymin, ymax)) +
facet_wrap(~ year) +
theme(axis.text.x = element_text(angle = 90)) +
NULL
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> filter: removed 5,707 rows (61%), 3,666 rows remaining
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
drop_na(oxy) %>%
group_by(quarter) %>%
summarise(min = min(oxy),
max = max(oxy))
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> drop_na: removed 139 rows (1%), 9,234 rows remaining
#> group_by: one grouping variable (quarter)
#> summarise: now 2 rows and 3 columns, ungrouped
#> # A tibble: 2 × 3
#> quarter min max
#> <int> <dbl> <dbl>
#> 1 1 -2.76 9.59
#> 2 4 -3.41 8.22
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
drop_na(temp) %>%
group_by(quarter) %>%
summarise(min = min(temp),
max = max(temp))
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> drop_na: removed 139 rows (1%), 9,234 rows remaining
#> group_by: one grouping variable (quarter)
#> summarise: now 2 rows and 3 columns, ungrouped
#> # A tibble: 2 × 3
#> quarter min max
#> <int> <dbl> <dbl>
#> 1 1 0.627 12.1
#> 2 4 3.53 14.5
dat_full %>%
distinct(haul.id, .keep_all = TRUE) %>%
drop_na(sal) %>%
group_by(quarter) %>%
summarise(min = min(sal),
max = max(sal))
#> distinct: removed 1,750,236 rows (99%), 9,373 rows remaining
#> drop_na: removed 159 rows (2%), 9,214 rows remaining
#> group_by: one grouping variable (quarter)
#> summarise: now 2 rows and 3 columns, ungrouped
#> # A tibble: 2 × 3
#> quarter min max
#> <int> <dbl> <dbl>
#> 1 1 6.67 23.8
#> 2 4 6.52 22.9Add UTM coords
# First add UTM coords
# Add UTM coords
utm_coords <- LongLatToUTM(dat_full$lon, dat_full$lat, zone = 33)
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition
dat_full$X <- utm_coords$X/1000 # for computational reasons
dat_full$Y <- utm_coords$Y/1000 # for computational reasonsSave data
write.csv(dat_full, file = "data/clean/catch_by_length_q1_q4.csv", row.names = FALSE)Compare cod data with Orio et al (2017)
knitr::knit_exit()